Input: ['The waiter was attentive , the food was delicious and the views of the city were great .']

Output: [['waiter', 'service general', 'positive', 'attentive'], ['food', 'food quality', 'positive', 'delicious'], ['views of the city', 'location 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: ['This establishment is the real deal .']

Output: [['establishment', 'restaurant general', 'positive', 'real deal']]

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 it seemed possible to do so while there I would have fought my bill since my dinner portion of my meal was inedible !']

Output: [['meal', 'food quality', 'negative', 'inedible'], ['NULL', 'restaurant prices', 'negative', 'fought my bill']]

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 quite a spectacular scene i 'll give them that ."]

Output: [['scene', 'ambience general', 'positive', 'spectacular']]

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 come here enjoy very much with husband .']

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

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: ["Do n't go with a larger group than 4 !"]

Output: [['NULL', 'restaurant miscellaneous', 'negative', "Do n't go"]]

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: ['Dessert : pure disaster .']

Output: [['Dessert', 'food quality', 'negative', 'disaster']]

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: ['Expensive']

Output: [['NULL', 'restaurant prices', 'negative', 'Expensive']]

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 daughter 's wedding reception at Water 's Edge received the highest compliments from our guests ."]

Output: [["Water 's Edge", 'restaurant miscellaneous', 'positive', 'highest compliments']]

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 survives on reputation alone .']

Output: [['place', 'restaurant miscellaneous', 'negative', 'survives on reputation alone']]

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 honestly do n't even know where to begin ."]

Output: [['NULL', 'restaurant general', 'negative', "do n't even know where to begin"]]

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 worked in restaurants and cook a lot , and there is no way a maggot should be able to get into well prepared food .']

Output: [['food', 'food quality', 'negative', 'well prepared']]

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: ['Once we finally got a table , despite indicating we wanted an alla carte menu we were pushed into a table that was only price fixed !']

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

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: ['Everyone raved about the atmosphere ( elegant rooms and absolutely incomparable views ) and the fabulous food !']

Output: [['atmosphere', 'ambience general', 'positive', 'raved'], ['rooms', 'ambience general', 'positive', 'elegant'], ['views', 'location general', 'positive', 'incomparable'], ['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: ["I 've enjoyed 99 % of the dishes we 've ordered with the only exceptions being the occasional too-authentic-for-me dish ( I 'm a daring eater but not THAT daring ) ."]

Output: [['dishes', 'food quality', 'positive', 'enjoyed'], ['dish', 'food quality', 'negative', 'too-authentic-for-me']]

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: ['Dokebi gives Williamsburg the right one-two punch of classic Korean food and fusion twists like pork belly tacos .']

Output: [['Korean food', 'food quality', 'positive', 'classic'], ['fusion twists', 'food quality', 'positive', 'classic'], ['pork belly tacos', 'food quality', 'positive', 'classic']]

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 we ordered was excellent , although I would n't say the margaritas were anything to write home about ."]

Output: [['food', 'food quality', 'positive', 'excellent'], ['margaritas', 'drinks quality', 'neutral', 'anything to write home about']]

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 an awful experience at Casa la Femme on a Saturday dinner .']

Output: [['Casa la Femme', 'restaurant general', 'negative', 'awful']]

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 bibimbap was average , but the stone bowl was n't even close to sizzling ."]

Output: [['bibimbap', 'food quality', 'neutral', 'average'], ['stone bowl', 'food quality', 'negative', "was n't even close to sizzling"]]

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 menu is fairly simple without much descriptions .']

Output: [['menu', 'food style_options', 'neutral', 'simple']]

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 took one look at the chicken and I was appalled .']

Output: [['chicken', 'food style_options', 'negative', 'appalled']]

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 find in the West Village !']

Output: [['NULL', '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 sauce tasted more like Chinese fast food than decent Korean .']

Output: [['sauce', 'food quality', 'negative', 'Chinese fast food']]

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: ['overpriced japanese food with mediocre service']

Output: [['japanese food', 'food prices', 'negative', 'overpriced'], ['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: ['We were drawn into the belly dancing show that captivated the crowd .']

Output: [['belly dancing show', 'ambience general', 'positive', 'captivated']]

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: ['Nice view of river and NYC .']

Output: [['view of river and NYC', 'location general', 'positive', 'Nice']]

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 design of the space is good .']

Output: [['space', 'ambience 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: ['its just a fun place to go , not a five star restaraunt .']

Output: [['restaraunt', 'restaurant general', 'neutral', 'five star']]

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: ['im not necessarily fanatical about this place , but it was a fun time for low pirces .']

Output: [['place', 'restaurant general', 'positive', 'fanatical'], ['place', 'restaurant prices', 'positive', 'fanatical']]

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: ['Food took some time to prepare , all worth waiting for .']

Output: [['Food', 'food quality', 'positive', 'worth'], ['NULL', 'service general', 'neutral', 'took some time']]

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: ['When I got there the place was packed but they made sure to seat me quickly .']

Output: [['NULL', 'service general', 'positive', 'seat me quickly ']]

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 almost amused by the fact that she was turning away customers at 9pm on a Friday night because she `` had a BBQ to go to `` that night - WTF ? ?']

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

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: ['Got a date ? Go here !']

Output: [['NULL', 'restaurant miscellaneous', 'positive', 'Go here']]

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: ['So I decide to report back to the waitress because it was completely inedible .']

Output: [['NULL', 'food quality', 'negative', 'inedible']]

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: ['awesome']

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

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: ["At $ 120 for two people , however , this in no way represents value , unless you 're looking to pay by the hour for passive-aggressive torture ."]

Output: [['NULL', 'restaurant prices', 'negative', 'no way represents value'], ['NULL', 'service general', 'negative', 'torture']]

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 pasta was well cooked , did n't have enough sauce though or flavor ."]

Output: [['pasta', 'food quality', 'positive', 'well cooked'], ['pasta', 'food quality', 'negative', "did n't have enough sauce"]]

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 real problem I had with this place was the complete lack of service .']

Output: [['service', 'service general', 'negative', 'lack 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: ['All in all , I would return - as it was a beautiful restaurant - but I hope the staff pays more attention to the little details in the future .']

Output: [['restaurant', 'restaurant general', 'positive', 'beautiful'], ['restaurant', 'ambience general', 'positive', 'beautiful'], ['staff', 'service general', 'negative', 'pays more attention']]

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 this place , every time we are in the city this is one of the places we always go .']

Output: [['place', '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: ['Reliable , Fresh Sushi']

Output: [['Sushi', 'food quality', 'positive', 'Reliable']]

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 really good pizza .']

Output: [['pizza', '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: ["I 'll be back for sure ."]

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: ["Do n't get me started on the margaritas , either ."]

Output: [['margaritas', 'drinks quality', 'negative', "Do n't get me started"]]

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 , our $ 14 drinks were were horrible !']

Output: [['drinks', 'drinks quality', 'negative', 'horrible'], ['drinks', 'drinks prices', '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: ['you know what i mean all the positives things happening there made mw write this review .']

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

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: ["Each time we 've been , the front of house staff ( not the waiters - they 're fantastic - but the people who greet and seat you ) has been so hideous to us that were it not for the exceptional fish dishes I would never return ."]

Output: [['waiters', 'service general', 'positive', 'fantastic'], ['front of house staff', 'service general', 'negative', 'hideous'], ['fish dishes', 'food quality', 'positive', 'exceptional']]

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 never ate because by close to 2 in the monring we were not served and were too upset ad tired to start eating .']

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

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: ['A coworker and I tried Pacifico after work a few Fridays and loved it .']

Output: [['Pacifico', 'restaurant general', 'positive', 'loved']]

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: ['Food was OK .']

Output: [['Food', 'food quality', 'neutral', 'OK']]

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: ['Can ’ t believe how an expensive NYC restaurant can be so disrespectful to its clients .']

Output: [['restaurant', 'restaurant prices', 'negative', 'expensive'], ['NULL', 'service general', 'negative', 'disrespectful']]

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: ['Creative , consistent , fresh .']

Output: [['NULL', 'food quality', 'positive', 'consistent'], ['NULL', 'food style_options', 'positive', 'Creative'], ['NULL', 'food quality', 'positive', 'fresh']]

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 crust was imazingly cooked well and pizza was fully loaded : ) : ) : )']

Output: [['crust', 'food quality', 'positive', 'cooked well'], ['pizza', 'food style_options', 'positive', 'fully loaded']]

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 was , from start to finish , a mind-bogglingly uncomfortable experience .']

Output: [['NULL', 'restaurant general', 'negative', 'mind-bogglingly uncomfortable']]

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 literally just got back home after visiting Casa La Femme and was so offended by my visit felt it necessary to try and warn other diners who value their money and time .']

Output: [['Casa La Femme', 'restaurant general', 'negative', 'offended'], ['Casa La Femme', 'restaurant prices', 'negative', 'offended']]

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: ['never swaying , never a bad meal , never bad service ...']

Output: [['NULL', 'restaurant general', 'positive', 'never swaying'], ['meal', 'food quality', 'positive', 'never a bad'], ['service', 'service general', 'positive', 'never bad']]

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 have a delicious banana chocolate dessert , as well as a great green tea tempura .']

Output: [['banana chocolate dessert', 'food quality', 'positive', 'delicious'], ['green tea tempura', '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: ['This is the MOST wonderful restaurant in all of New York City , not just Brooklyn ...']

Output: [['restaurant', 'restaurant general', '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: ['Overpriced and not tasty']

Output: [['NULL', 'food prices', 'negative', 'Overpriced'], ['NULL', 'food quality', 'negative', 'not tasty']]

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 waiter delivered our food while holding what appeared to be a plastic bag of garbage in one hand .']

Output: [['waiter', 'service general', 'negative', 'garbage']]

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 are extremely rude , not even apologizing for the horrible service we got and handing us a bill well over $ 500 for some drinks adn their pita bread !']

Output: [['service', 'service general', 'negative', 'horrible'], ['drinks', 'drinks prices', 'negative', 'over $ 500'], ['pita bread', 'food prices', 'negative', 'over $ 500'], ['NULL', 'service general', 'negative', 'rude']]

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: ['so 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: ['Stay away']

Output: [['NULL', 'restaurant general', 'negative', 'Stay away']]

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 . Sushi . Ever .']

Output: [['Sushi', '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: ['Raymond the bartender rocks !']

Output: [['Raymond', 'service general', 'positive', 'rocks']]

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 hostess was rude and I got a distinct feeling that they did not want to serve us .']

Output: [['hostess', 'service general', 'negative', 'rude'], ['NULL', 'service general', 'negative', 'distinct']]

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 wanted to go there to see if it was worth it and sadly , curiousity got the best of me and I paid dearly for it .']

Output: [['NULL', 'restaurant general', 'negative', 'sadly'], ['NULL', 'restaurant prices', 'negative', 'paid dearly for it']]

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: ['Look , the appetizers were really good .']

Output: [['appetizers', '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 food and service were fine , however the maitre-D was incredibly unwelcoming and arrogant .']

Output: [['food', 'food quality', 'positive', 'fine'], ['service', 'service general', 'positive', 'fine'], ['maitre-D', 'service general', 'negative', 'unwelcoming'], ['maitre-D', 'service general', 'negative', 'arrogant']]

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 flavors are amazing and the value is phenomenal .']

Output: [['NULL', 'food quality', 'positive', 'amazing'], ['NULL', 'food prices', 'positive', 'phenomenal']]

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 potato balls were not dry at all ... in fact it was buttery .']

Output: [['potato balls', 'food quality', 'positive', 'not dry']]

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 toppings definitely a place you need to check out for late night munchies or a mid day boost !']

Output: [['toppings', 'food quality', 'positive', 'great'], ['place', 'restaurant miscellaneous', '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 food tasted very good .']

Output: [['food', '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: ['Overall , decent food at a good price , with friendly people .']

Output: [['food', 'food quality', 'positive', 'decent'], ['food', 'food prices', 'positive', 'good'], ['people', '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: ["I do n't appreciate places or people that try to drive up the bill without the patron 's knowledge so that was a huge turnoff ( more than the price ) ."]

Output: [['NULL', 'service general', 'negative', 'drive up the bill'], ['NULL', 'restaurant prices', 'negative', 'huge turnoff']]

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: ['A quintessential slice of NYC pizza .']

Output: [['slice of NYC pizza', 'food quality', 'neutral', 'quintessential']]

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 each course we waited over 1 /2 hour to 45 minutes and were never offered a drink .']

Output: [['NULL', 'service general', 'negative', 'waited over 1 /2 hour to 45 minutes']]

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: ['Super YUMMY Pizza !']

Output: [['Pizza', '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: ['would 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: ["All the various Greek and Cypriot dishes are excellent , but the gyro is the reason to come -- if you do n't eat one your trip was wasted ."]

Output: [['Greek and Cypriot dishes', 'food quality', 'positive', 'excellent'], ['gyro', 'food quality', 'positive', 'the reason to come']]

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: ['Wish NY had more of these kind of places : intimate , superb food , homey , top notch all the way around , certainly worth the wait .']

Output: [['NULL', 'ambience general', 'positive', 'intimate'], ['food', 'food quality', 'positive', 'superb'], ['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: ['Bukhara is on my top 5 Indian places in NYC']

Output: [['Bukhara', 'restaurant general', 'positive', 'top']]

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: ['Service not the friendliest to our `` large party `` !']

Output: [['Service', 'service general', 'negative', 'not the friendliest']]

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 CAN EAT HERE EVERY DAY OF THE WEEK REALLY LOL LOVE THIS PLACE ... )']

Output: [['PLACE', '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: ['Best Indian Chinese in the city , by far !']

Output: [['Indian Chinese', '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: ["There 's another girl who I ca n't describe , she is about 5 ' 6 `` with brown hair , who eavesdrops on your conversation and chimes in - except she only hears the last part of what you said , so her uninvited opinions are often out of context and nothing to do with what you 're *really * talking about ."]

Output: [['girl', 'service general', 'negative', 'uninvited']]

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 was quick and friendly .']

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: ['On the way out , we heard of other guests complaining about similar issues .']

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

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 sake ’ s complimented the courses very well and is successfully easing me into the sake world .']

Output: [['sake ’ s', 'drinks quality', 'positive', 'very well'], ['sake ’ s', 'drinks quality', 'positive', 'successfully']]

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: ['Fresh , mind blowing flavors .']

Output: [['NULL', 'food quality', 'positive', 'Fresh'], ['NULL', 'food quality', 'positive', 'mind blowing']]

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: ['Dumbfoundingly Poor']

Output: [['NULL', 'restaurant general', 'negative', 'Dumbfoundingly 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: ['All in all the food was good - a little on the expensive side , but fresh .']

Output: [['food', 'food quality', 'positive', 'fresh'], ['food', 'food quality', 'positive', 'good'], ['food', 'food prices', 'negative', 'expensive']]

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: ['Sushi experience was unbelievable with my fiance .']

Output: [['Sushi', 'food quality', 'positive', 'unbelievable']]

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: ['Single Worst Restaurant in Manhattan']

Output: [['Restaurant', 'restaurant 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: ["Patsy 's Pizza - true love"]

Output: [["Patsy 's Pizza", 'restaurant general', 'positive', 'true 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: ['What really makes it shine is the food , which is aggressively seasoned with Cyrpriot spices , and all made in-house ( even the gyro meat and sausages ) , and made of much higher quality ingredients that might otherwise be expected .']

Output: [['food', 'food quality', 'positive', 'shine'], ['gyro meat', 'food quality', 'positive', 'in-house'], ['sausages', 'food quality', 'positive', 'in-house'], ['ingredients', 'food quality', 'positive', 'higher quality']]

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 Indian Food !']

Output: [['Indian Food', '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: ['The appetizers we ordered were served quickly - an order of fried oysters and clams were delicious but a tiny portion ( maybe 3 of each ) .']

Output: [['fried oysters and clams', 'food quality', 'positive', 'delicious'], ['fried oysters and clams', 'food style_options', 'negative', 'tiny portion'], ['NULL', 'service general', 'positive', 'served quickly']]

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 main concern was the sanity of the food that was being sent out to myself and others , but I would be lying is I said that as someone who has worked in restaurants since the age of fifteen I was expecting at least a minimal effort on the part of the restaurant to amend the situation .']

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

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: ['Prices are fair across the board for both food and bev .']

Output: [['food', 'food prices', 'positive', 'fair'], ['bev', 'drinks prices', 'positive', 'fair']]

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: ['One special roll and one regular roll is enough to fill you up , but save room for dessert !']

Output: [['dessert', 'food quality', 'positive', 'save room'], ['special roll', 'food style_options', 'positive', 'enough'], ['regular roll', 'food style_options', 'positive', 'enough']]

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: ['On our last visit , they skipped over our name on the list , leaving us waiting an extra hour for a table .']

Output: [['NULL', 'service general', 'negative', 'waiting an extra 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: ['Try it !']

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

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: ['Favorite Sushi in NYC']

Output: [['Sushi', 'food quality', '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: ['The wine list was extensive - though the staff did not seem knowledgeable about wine pairings .']

Output: [['wine list', 'drinks style_options', 'positive', 'extensive'], ['staff', 'service general', 'negative', 'not seem knowledgeable']]

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: ["Service was just ok , it is not what you 'd expect for $ 500 ."]

Output: [['Service', 'service general', 'negative', 'ok'], ['NULL', 'restaurant prices', 'negative', '$ 500']]

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 Four Seasons restaurant is a great experience .']

Output: [['The Four Seasons restaurant', '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: ['An unpretentious spot in Park Slope , the sushi is consistently good , the service is pleasant , effective and unassuming .']

Output: [['spot', 'ambience general', 'positive', 'unpretentious'], ['sushi', 'food quality', 'positive', 'good'], ['service', 'service general', 'positive', 'pleasant'], ['service', 'service general', 'positive', 'effective'], ['service', 'service general', 'positive', 'unassuming']]

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: ['In the summer months , the back garden area is really nice .']

Output: [['back garden area', 'ambience general', 'positive', 'nice']]

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 are going for the food , it will not be worth it .']

Output: [['food', 'food quality', 'negative', '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: ['The Cypriot restaurant has a lot going for it .']

Output: [['Cypriot restaurant', 'restaurant general', 'positive', 'a lot going']]

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 service is HORRID !']

Output: [['service', 'service general', 'negative', 'HORRID']]

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 the food quality was pretty good , though I hear the salmon is much better when it has n't sat cooling in front of the guest ."]

Output: [['food', '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 food .']

Output: [['food', '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: ['AMAZING MY FAVORITE ! ! !']

Output: [['NULL', 'restaurant general', '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: ['I generally like this place .']

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

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 survice']

Output: [['survice', 'service 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: ['Highly impressed from the decor to the food to the hospitality to the great night I had !']

Output: [['decor', 'ambience general', 'positive', 'impressed'], ['food', 'food quality', 'positive', 'impressed'], ['NULL', 'service general', 'positive', 'hospitality'], ['NULL', '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']]'