Input: ['Hands down , the best tuna I have ever had .']

Output: [['tuna', 'food quality', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["If you 're going to see Danny Gans or just staying at the Mirage , do n't miss this one ."]

Output: [['NULL', 'restaurant general', 'positive', "do n't miss"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['great lunch spot']

Output: [['lunch spot', 'restaurant general', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I love breakfast here .']

Output: [['breakfast', 'food quality', 'positive', 'love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The sashimi was the freshest and most tender I have ever tasted .']

Output: [['sashimi', 'food quality', 'positive', 'freshest']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['You won ’ t be disappointed by their menu .']

Output: [['menu', 'food style_options', 'positive', 'won ’ t be disappointed']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Many people talk about the great pizza and poor service , so it ca n't just be the rantings of a few dissatisfied customers ."]

Output: [['pizza', 'food quality', 'positive', 'great'], ['service', 'service general', 'negative', 'poor']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I will not go back .']

Output: [['NULL', 'restaurant general', 'negative', 'not go back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['my service was stellar !']

Output: [['service', 'service general', 'positive', 'stellar']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['we are for sure coming back to this restaurant .']

Output: [['restaurant', 'restaurant general', 'positive', 'coming back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Will definitely be back .']

Output: [['NULL', 'restaurant general', 'positive', 'be back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Maybe it is good for that one night once in a blue moon when the chefs decide to use fish that 's half-way decent ."]

Output: [['fish', 'food quality', 'negative', 'decent']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["– I 've been to Open Sesame only once , but I 'm still reeling from the experience ! !"]

Output: [['Open Sesame', 'restaurant general', 'positive', 'reeling']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The pancakes were certainly inventive but $ 8.50 for 3 - 6 `` pancakes ( one of them was more like 5 `` ) in the pancake flight ( sample of 3 different pancakes ) is well over-priced .']

Output: [['pancakes', 'food style_options', 'positive', 'inventive'], ['pancakes', 'food prices', 'negative', 'over-priced']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['HOWEVER , one Sunday afternoon my husband and I did go ( although with my loud protests ) and were pleasantly surprised .']

Output: [['NULL', 'restaurant general', 'positive', 'pleasantly surprised']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Well worth it']

Output: [['NULL', 'restaurant general', 'positive', 'worth']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Caution - its real food for people who love the best .']

Output: [['food', 'food quality', 'positive', 'real']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I have a but here - there was a bathroom attendant in the restroom which was odd .']

Output: [['restroom', 'restaurant miscellaneous', 'negative', 'odd']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The best dish are the honwy walnut prawns -- just outstanding .']

Output: [['honwy walnut prawns', 'food quality', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The pancakes should be larger ( at least 8 `` ) to justify the expense even with the unique offerings .']

Output: [['pancakes', 'food style_options', 'negative', 'should be larger']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– My first time to dine at this restaurant was with my son and it was absolutely horrible !']

Output: [['restaurant', 'restaurant general', 'negative', 'horrible']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Excellent']

Output: [['NULL', 'restaurant general', 'positive', 'Excellent']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Parking is reasonably good , they have their own lot and you can park in the park nearby .']

Output: [['NULL', 'restaurant miscellaneous', 'positive', 'reasonably good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Caesar salad was superb .']

Output: [['Caesar salad', 'food quality', 'positive', 'superb']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["– I do n't understand how I was a stranger to this place for so long ... the fajita salad , the colorado , the fajitas - EVERYTHING is delicious ."]

Output: [['fajita salad', 'food quality', 'positive', 'delicious'], ['colorado', 'food quality', 'positive', 'delicious'], ['fajitas', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['best chinese food i have tasted in a long time']

Output: [['chinese food', 'food quality', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The price was right too .']

Output: [['NULL', 'restaurant prices', 'positive', 'right']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Delicious ... !']

Output: [['NULL', 'food quality', 'positive', 'Delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["We put out name down and although there were open tables we were told that it 'd be a 30 minute wait ."]

Output: [['NULL', 'service general', 'negative', 'wait']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The coffe is very good , too .']

Output: [['coffe', 'drinks quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We waited over 30 minutes for our drinks and over 1 1 /2 hours for our food .']

Output: [['NULL', 'service general', 'negative', 'waited']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The servers behind the counter are always friendly and helpful .']

Output: [['servers behind the counter', 'service general', 'positive', 'friendly']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Our waiter was non-existent and after our food finally arrived over an hour after we ordered , we were not given any water or utensils .']

Output: [['waiter', 'service general', 'negative', 'non-existent']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– This place is unbelievably over-rated .']

Output: [['place', 'restaurant general', 'negative', 'over-rated']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Very poor customer service .']

Output: [['customer service', 'service general', 'negative', 'poor']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We ’ re can ’ t say enough about their delicious gourmet pizza ’ s !']

Output: [['pizza ’ s', 'food quality', 'positive', 'delicious']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Furthermore , while the fish is unquestionably fresh , rolls tend to be inexplicably bland .']

Output: [['fish', 'food quality', 'positive', 'fresh'], ['rolls', 'food quality', 'negative', 'bland']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The only drawback is the crowded seating and the slow service .']

Output: [['seating', 'restaurant miscellaneous', 'negative', 'crowded'], ['service', 'service general', 'negative', 'slow']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Not alot of smoking places left in New York , but I have found my favorite smoking balconey in the city .']

Output: [['balconey', 'restaurant miscellaneous', 'positive', 'favorite']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We shared the family platter and I especially enjoyed the black cod in sake kasu .']

Output: [['black cod in sake kasu', 'food quality', 'positive', 'enjoyed']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['But the space is small and lovely , and the service is helpful .']

Output: [['space', 'ambience general', 'positive', 'small'], ['space', 'ambience general', 'positive', 'lovely'], ['service', 'service general', 'positive', 'helpful']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['It was worth the wait .']

Output: [['NULL', 'restaurant general', 'positive', 'worth']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['yummy .']

Output: [['NULL', 'food quality', 'positive', 'yummy']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Delectable']

Output: [['NULL', 'food quality', 'positive', 'Delectable']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['- Mediocre Service / Quality']

Output: [['Service', 'service general', 'neutral', 'Mediocre']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– This is one of my top lunch spots , huge portions , fast service and amazing margaritas ! !']

Output: [['NULL', 'restaurant general', 'positive', 'top lunch spots'], ['portions', 'food style_options', 'positive', 'huge'], ['service', 'service general', 'positive', 'fast'], ['margaritas', 'drinks quality', 'positive', 'amazing']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Best Crab Cakes in Town']

Output: [['Crab Cakes', 'food quality', 'positive', 'Best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Everything I 've had here is good , taco salads , burritos , enchiladas i love this place ."]

Output: [['taco salads', 'food quality', 'positive', 'good'], ['burritos', 'food quality', 'positive', 'good'], ['enchiladas', 'food quality', 'positive', 'good'], ['place', 'restaurant general', 'positive', 'love'], ['NULL', 'food quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Great Pizza , Poor Service']

Output: [['Pizza', 'food quality', 'positive', 'Great'], ['Service', 'service general', 'negative', 'Poor']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['splendid']

Output: [['NULL', 'restaurant general', 'positive', 'splendid']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['My best friend had the chicken shawarma and she STILL raves about it being the best anywhere !']

Output: [['chicken shawarma', 'food quality', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Chintzy portions']

Output: [['portions', 'food style_options', 'negative', 'Chintzy']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Good eats .']

Output: [['eats', 'food quality', 'positive', 'Good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["Normally , places ask how hot you want it , but they did n't ."]

Output: [['NULL', 'service general', 'negative', "but they did n't"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I 've always found the wait staff and , if you sit at the bar , the cooks very friendly ."]

Output: [['cooks', 'service general', 'positive', 'friendly']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Nothing on the menu is less than amazing .']

Output: [['menu', 'food quality', 'positive', 'amazing']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["My party of two was feeling particularly ambitious , and we splurged on the Shilshole Sampler ... a beautiful assortment of enormous white gulf prawns , smoked albacore tuna , Ray 's fantastic manila clams seasoned with dill , scallops in a tasty soy dressing , and a tiny pile of Dungeness crab atop a sublime butter sauce ."]

Output: [['white gulf prawns', 'food style_options', 'positive', 'enormous'], ['manila clams', 'food quality', 'positive', 'fantastic'], ['soy dressing', 'food quality', 'positive', 'tasty'], ['butter sauce', 'food quality', 'positive', 'sublime'], ['Shilshole Sampler', 'food style_options', 'positive', 'a beautiful assortment']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I was frankly shocked when I read the bad reviews - this place is fantastic ; it has not let us down in any way , and we 've eaten here more than 10 times ."]

Output: [['place', 'restaurant general', 'positive', 'fantastic']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Your a sushi fan , you love expertly cut fish , great sake , a killer SOHO location , and of course : Salmon , Tuna , Fluke , Yellow Tail , Cod , Mackeral , Jellyfish , Sea Urchin , Shrimp , Lobster , Sea Bream , Trout , Milk Fish , Blue Fin Tuna , Eel , Crab , Sardine , Monk Fish , Roe , Scallop , Oysters , and a varity of Toro .']

Output: [['sushi', 'food quality', 'positive', 'fan'], ['fish', 'food style_options', 'positive', 'love expertly cut'], ['sake', 'drinks quality', 'positive', 'great'], ['SOHO location', 'location general', 'positive', 'killer'], ['Toro', 'food style_options', 'positive', 'a varity of']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The service is fantastic at this fun place .']

Output: [['service', 'service general', 'positive', 'fantastic'], ['place', 'restaurant general', 'positive', 'fun']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Also , there was only one bathroom stall- probably need more for such big crowds .']

Output: [['bathroom stall', 'restaurant miscellaneous', 'negative', 'need more']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We left without ever getting service .']

Output: [['service', 'service general', 'negative', 'without ever']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I liked it .']

Output: [['NULL', 'restaurant general', 'positive', 'liked']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– I highly recommend Mioposto .']

Output: [['Mioposto', 'restaurant general', 'positive', 'recommend']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food is fantastic , authentic , delicious and very , very affordable .']

Output: [['food', 'food quality', 'positive', 'fantastic'], ['food', 'food quality', 'positive', 'authentic'], ['food', 'food quality', 'positive', 'delicious'], ['food', 'food prices', 'positive', 'affordable']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I have eaten here three times and have found the quality and variety of the fish to be excellent .']

Output: [['fish', 'food quality', 'positive', 'excellent'], ['fish', 'food style_options', 'positive', 'excellent']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['But she is very friendly with certain people , making it even more annoying .']

Output: [['NULL', 'service general', 'negative', 'annoying']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The regular menu here is slightly above average that is not worth the snotty attitude that you receive .']

Output: [['regular menu', 'food quality', 'neutral', 'above average'], ['regular menu', 'food quality', 'neutral', 'not worth the snotty attitude'], ['NULL', 'service general', 'negative', 'snotty']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Also , they serve THE best hummus in America , with a drizzle of fragrant olive oil ( which , I believe is the traditional way ) !']

Output: [['hummus', 'food quality', 'positive', 'best'], ['hummus', 'food style_options', 'positive', 'best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['great beer']

Output: [['beer', 'drinks quality', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Seafood Plus']

Output: [['Seafood', 'food quality', 'positive', 'Plus']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['it is sad there is not many people who frequent eating at places like these that look pricey because they are at the hotel ... but they are definitely one you dont want to miss , esp if your in downtown san jose .']

Output: [['NULL', 'restaurant general', 'positive', 'you dont want to miss']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['They serve it in a tall , skinny hour-glass shaped glass to disguise the fact that you a getting a small juice at the price of a half gallon in a supermarket .']

Output: [['juice', 'drinks style_options', 'negative', 'small'], ['juice', 'drinks prices', 'negative', 'small']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The food was ok , but the service was so poor that the food was cold buy the time everyone in my party was served .']

Output: [['food', 'food quality', 'neutral', 'ok'], ['service', 'service general', 'negative', 'poor']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['great meal – the fish on the omikase platter was absolutely decadent -- there was none of the stringiness that sometimes accompanies fair sushi -- this fish was perfect ! ! ! !']

Output: [['meal', 'food quality', 'positive', 'great'], ['fish on the omikase platter', 'food quality', 'positive', 'decadent'], ['fish on the omikase platter', 'food quality', 'positive', 'perfect']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Drinks are suberb , and I feel like I am in a Third World country when I walk in the door .']

Output: [['Drinks', 'drinks quality', 'positive', 'suberb'], ['NULL', 'ambience general', 'positive', 'in a Third World country']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Open & Cool Place with the Best Pizza and Coffee']

Output: [['Place', 'ambience general', 'positive', 'Cool'], ['Pizza', 'food quality', 'positive', 'Best'], ['Coffee', 'drinks quality', 'positive', 'Best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Green Tea creme brulee is a must !']

Output: [['Green Tea creme brulee', 'food quality', 'positive', 'must']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I found a new home and just moved with a long lease .']

Output: [['NULL', 'restaurant general', 'positive', 'new home']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["They aren 't the most talkative , but everytime I 've been there they have been very busy , which probably accounts for the lack of conversation ."]

Output: [['NULL', 'service general', 'neutral', "aren 't the most talkative"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["The pizza itself is not exactly the best I 've had EVER , but still pretty good ."]

Output: [['pizza', 'food quality', 'positive', 'not exactly the best']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We did have the same waiter the second time , so maybe the service is spotty and our luck is good .']

Output: [['waiter', 'service general', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["– Thats a big statement considering I 've been pulling crab traps and making the cakes myself since I was about seven - but something about these little devils gets better every time ."]

Output: [['cakes', 'food quality', 'positive', 'better']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Fabulous Italian Food !']

Output: [['Italian Food', 'food quality', 'positive', 'Fabulous']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["However , this place is a gem , and I wo n't stop going back ."]

Output: [['place', 'restaurant general', 'positive', 'gem']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['it gets really busy , so get there on the early side so you can grab a seat , if you do have to wait , its not bad because the service is quick !']

Output: [['service', 'service general', 'positive', 'quick']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The dinner only took us about an hour and the prices were alright for the strip , almost $ 50 /pp after tax and tip ( about average ) .']

Output: [['NULL', 'restaurant prices', 'neutral', 'alright']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The onion rings are great !']

Output: [['onion rings', 'food quality', 'positive', 'great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["If you 're in the area you shouldn 't be disappointed ."]

Output: [['NULL', 'restaurant general', 'positive', "shouldn 't be disappointed"]]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– Great financial district mexican spot .']

Output: [['mexican spot', 'restaurant general', 'positive', 'Great']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The house special roll is really good .']

Output: [['house special roll', 'food quality', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The waiters are very experienced and helpful with pairing your drink choice to your food tastes or vice versa .']

Output: [['waiters', 'service general', 'positive', 'experienced']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Perfection .']

Output: [['NULL', 'restaurant general', 'positive', 'Perfection']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Poor customer service / poor pizza .']

Output: [['customer service', 'service general', 'negative', 'Poor'], ['pizza', 'food quality', 'negative', 'Poor']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Overall , I would go back and eat at the restaurant again .']

Output: [['restaurant', 'restaurant general', 'positive', 'go back']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Server made several sake suggestions which were very good .']

Output: [['sake', 'drinks quality', 'positive', 'good'], ['Server', 'service general', 'positive', 'suggestions']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['My only negative comment is that I wish the pieces were a little bigger .']

Output: [['pieces', 'food style_options', 'negative', 'negative']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I picked the asparagus , which turned out to be incredible and perfectly prepared .']

Output: [['asparagus', 'food quality', 'positive', 'incredible']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['What may be interesting to most is the worst sevice /attitude comes from the owners of this establishment .']

Output: [['owners', 'service general', 'negative', 'worst']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The only drawback is that this place is really expensive and the portions are on the small side .']

Output: [['place', 'restaurant prices', 'negative', 'expensive'], ['portions', 'food style_options', 'negative', 'small']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Terrible service , food ok , pricey']

Output: [['service', 'service general', 'negative', 'Terrible'], ['food', 'food quality', 'neutral', 'ok'], ['NULL', 'restaurant prices', 'negative', 'pricey']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Although the service could be improved considering the money you put in .']

Output: [['service', 'service general', 'negative', 'could be improved']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['You routinely see Indian families and friends dining there , which is always a good sign .']

Output: [['NULL', 'restaurant miscellaneous', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["If I want to stand in line on Sunday for an hour to get average brunch food , then I would put Murphy 's at the top of the list ."]

Output: [['brunch food', 'food quality', 'neutral', 'average'], ['NULL', 'service general', 'negative', 'an hour']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['For the amount of food we got the prices should have been lower .']

Output: [['food', 'food prices', 'negative', 'should have been lower']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Always busy , but they are good at seating you promptly and have quick service .']

Output: [['service', 'service general', 'positive', 'quick'], ['NULL', 'service general', 'positive', ' good at']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The wine list is wonderful and the food reminds me of my recent trip to Italy .']

Output: [['wine list', 'drinks style_options', 'positive', 'wonderful']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['the food was great , the margaritas too but the waitress was too busy being nice to her other larger party than to take better care of my friend and me .']

Output: [['food', 'food quality', 'positive', 'great'], ['margaritas', 'drinks quality', 'positive', 'great'], ['waitress', 'service general', 'negative', 'too busy']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["I ca n't saybenough good things about this restaurant , and I ca n't wait for my next several visits ."]

Output: [['restaurant', 'restaurant general', 'positive', 'good']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Love it every time']

Output: [['NULL', 'restaurant general', 'positive', 'Love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['We are locals , and get the feeling the only way this place survives with such average food is because most customers are probably one-time customer tourists .']

Output: [['food', 'food quality', 'negative', 'average']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['Also love their caeser salad .']

Output: [['caeser salad', 'food quality', 'positive', 'love']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["She doesn 't make you feel welcome and treats you like an annoyance ."]

Output: [['NULL', 'service general', 'negative', 'annoyance']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["It 's just the right size for the menu ."]

Output: [['NULL', 'restaurant miscellaneous', 'positive', 'the right size']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ["The food is fantastic , and the waiting staff has been perfect every single time we 've been there ."]

Output: [['food', 'food quality', 'positive', 'fantastic'], ['waiting staff', 'service general', 'positive', 'perfect']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['The desert was the perfect ending to an almost perfect dinner .']

Output: [['desert', 'food quality', 'positive', 'perfect'], ['dinner', 'food quality', 'positive', 'perfect']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I have been here , spent tons of money on a chef special dinner and it was a major dissappointment .']

Output: [['chef special dinner', 'food quality', 'negative', 'dissappointment'], ['chef special dinner', 'food prices', 'negative', 'dissappointment']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['I FOUND A NEW HOME ON SECOND AVENUE ! ! ! ! ! ! ! ! ! ! ! !']

Output: [['NULL', 'restaurant general', 'positive', 'NEW HOME']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'


Input: ['– Had to wait thirty minutes to get in the door on a tuesday morning , but it was so worth it .']

Output: [['NULL', 'restaurant miscellaneous', 'negative', 'wait thirty minutes'], ['NULL', 'restaurant general', 'positive', 'worth']]

Instruction: Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words. Input: A sentence. Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: "Null" means that there is no occurrence in the sentence. Example:  Sentence: "The ambience was so fun , and the prices were great , on top of the fact that the food was really tasty"  Output: [['ambience', 'ambience general', 'positive', 'fun'], ['NULL', 'restaurant prices', 'positive', 'great'], ['food', 'food quality', 'positive', 'tasty']]'