Datasets:

NEUDM

/

absa-quad

like 0

Follow

NEU-Datamining 20

generation

absa-quad

['Big thumbs up !']

[['NULL', 'restaurant general', 'positive', 'thumbs up']]

none

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']]'

generation

absa-quad

["I have been to Rao 's probably 15 times the past 3 years and it keeps getting better ."]

[["Rao 's", 'restaurant general', 'positive', 'better']]

none

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']]'

generation

absa-quad

['Bukhara Grill , the tagline says it all . . `` INDIAN SPICE RAVE ``']

[['Bukhara Grill', 'restaurant general', 'positive', 'RAVE']]

none

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']]'

generation

absa-quad

['MY husbands birthday and my sons was not as it was intended ... and we drove two hours to spend too much money to be treated terribly !']

[['NULL', 'restaurant prices', 'negative', 'too much money'], ['NULL', 'service general', 'negative', 'treated terribly']]

none

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']]'

generation

absa-quad

['The atmosphere was pretty nice but had a bit lacking , which it tries to make up for with a crazy scheme of mirrors .']

[['atmosphere', 'ambience general', 'negative', 'nice'], ['scheme of mirrors', 'ambience general', 'negative', 'crazy']]

none

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']]'

generation

absa-quad

['It was served with skin , over a bed of extremely undercooked spinach and mashed potatoes .']

[['NULL', 'food style_options', 'negative', 'served with skin'], ['spinach', 'food quality', 'negative', 'undercooked']]

none

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']]'

generation

absa-quad

['This place has totally weird decor , stairs going up with mirrored walls - I am surprised how no one yet broke their head or fall off the stairs - mirrored walls make you dizzy and delusional ...']

[['decor', 'ambience general', 'negative', 'weird'], ['mirrored walls', 'ambience general', 'negative', 'dizzy'], ['mirrored walls', 'ambience general', 'negative', 'delusional']]

none

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']]'

generation

absa-quad

['The food is amazing , rich pastas and fresh doughy pizza .']

[['food', 'food quality', 'positive', 'amazing'], ['pastas', 'food style_options', 'positive', 'rich'], ['pizza', 'food quality', 'positive', 'fresh doughy']]

none

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']]'

generation

absa-quad

['limited menu , no-so-fresh ingredients , thinly-sliced fish , fall-apart rice .']

[['menu', 'food style_options', 'negative', 'limited'], ['ingredients', 'food quality', 'negative', 'no-so-fresh'], ['fish', 'food style_options', 'negative', 'thinly-sliced'], ['rice', 'food style_options', 'negative', 'fall-apart']]

none

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']]'

generation

absa-quad

['I like the somosas , chai , and the chole , but the dhosas and dhal were kinda dissapointing .']

[['somosas', 'food quality', 'positive', 'like'], ['chai', 'food quality', 'positive', 'like'], ['chole', 'food quality', 'positive', 'like'], ['dhosas', 'food quality', 'negative', 'dissapointing'], ['dhal', 'food quality', 'negative', 'dissapointing']]

none

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']]'

generation

absa-quad

['Food is great .']

[['Food', 'food quality', 'positive', 'great']]

none

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']]'

generation

absa-quad

['The hot dogs are good , yes , but the reason to get over here is the fantastic pork croquette sandwich , perfect on its supermarket squishy bun .']

[['hot dogs', 'food quality', 'positive', 'good'], ['pork croquette sandwich', 'food quality', 'positive', 'fantastic'], ['bun', 'food quality', 'positive', 'perfect']]

none

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']]'

generation

absa-quad

['I highly recommend it .']

[['NULL', 'restaurant general', 'positive', 'recommend']]

none

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']]'

generation

absa-quad

['Great Indian Food !']

[['Indian Food', 'food quality', 'positive', 'Great']]

none

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']]'

generation

absa-quad

['Always good drinks and service is pretty good ;']

[['drinks', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]

none

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']]'

generation

absa-quad

['Highly recommend it !']

[['NULL', 'restaurant general', 'positive', 'recommend']]

none

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']]'

generation

absa-quad

['They are not helpful in the least and will give you the grand run around so by the time the event date rolls around you will not only regret chosing this place , but also become hostile !']

[['NULL', 'service general', 'negative', 'not helpful in the least']]

none

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']]'

generation

absa-quad

['The prices were CHEAP compared to the quality of service and food .']

[['NULL', 'restaurant prices', 'positive', 'CHEAP']]

none

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']]'

generation

absa-quad

['it is not consistent .']

[['NULL', 'restaurant general', 'negative', 'not consistent']]

none

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']]'

generation

absa-quad

["The food is amazing ... especially if you get the Chef 's tasting menu and your favourite bottle ( or two ! ) of wine from an extensive selection of wines ."]

[['food', 'food quality', 'positive', 'amazing'], ['selection of wines', 'drinks style_options', 'positive', 'extensive'], ["Chef 's tasting menu", 'food quality', 'positive', 'favourite']]

none

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']]'

generation

absa-quad

["The worst excuse for Japanese food I 've ever encountered ."]

[['NULL', 'restaurant miscellaneous', 'negative', 'worst']]

none

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']]'

generation

absa-quad

['I plan on stopping by next week as well .']

[['NULL', 'restaurant general', 'positive', 'stopping by next week']]

none

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']]'

generation

absa-quad

['supercilious scorn is in .']

[['NULL', 'service general', 'negative', 'supercilious scorn']]

none

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']]'

generation

absa-quad

['Someone else recommended the dessert - we also left that .']

[['dessert', 'food quality', 'negative', 'recommended']]

none

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']]'

generation

absa-quad

['This is the perfect date spot for Williamsburg couples .']

[['NULL', 'restaurant miscellaneous', 'positive', 'perfect']]

none

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']]'

generation

absa-quad

['Indoor was very cozy and cute .']

[['Indoor', 'ambience general', 'positive', 'cozy'], ['Indoor', 'ambience general', 'positive', 'cute']]

none

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']]'

generation

absa-quad

['The only beverage we did receive was water in dirty glasses !']

[['NULL', 'service general', 'negative', 'dirty']]

none

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']]'

generation

absa-quad

["It 's one of our favorite places to eat in NY ."]

[['NULL', 'restaurant general', 'positive', 'favorite']]

none

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']]'

generation

absa-quad

['Salads are a delicious way to begin the meal .']

[['Salads', 'food quality', 'positive', 'delicious']]

none

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']]'

generation

absa-quad

['wont come back again for sure !']

[['NULL', 'restaurant general', 'negative', 'wont come back']]

none

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']]'

generation

absa-quad

['This place has ruined me for neighborhood sushi .']

[['sushi', 'food quality', 'positive', 'ruined']]

none

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']]'

generation

absa-quad

['Me and my girls will definitely go back .']

[['NULL', 'restaurant general', 'positive', 'go back']]

none

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']]'

generation

absa-quad

['The large selection of bruschettas , paninis , tramezzinis keep the palate from stagnating .']

[['bruschettas', 'food style_options', 'positive', 'large selection'], ['paninis', 'food style_options', 'positive', 'large selection'], ['tramezzinis', 'food style_options', 'positive', 'large selection']]

none

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']]'

generation

absa-quad

['I HAVE NEVER EVER HAD SUCH AN UNPLEASANT EXPERIENCE .']

[['NULL', 'restaurant general', 'negative', 'UNPLEASANT']]

none

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']]'

generation

absa-quad

['We ordered the special , grilled branzino , that was so infused with bone , it was difficult to eat .']

[['grilled branzino', 'food quality', 'negative', 'difficult to eat']]

none

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']]'

generation

absa-quad

['The veal and the mushrooms were cooked perfectly .']

[['veal', 'food quality', 'positive', 'perfectly'], ['mushrooms', 'food quality', 'positive', 'perfectly']]

none

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']]'

generation

absa-quad

['My friend devoured her chicken and mashed potatos .']

[['chicken and mashed potatos', 'food quality', 'positive', 'devoured']]

none

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']]'

generation

absa-quad

['The food is all shared so we get to order together and eat together .']

[['food', 'food style_options', 'positive', 'shared']]

none

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']]'

generation

absa-quad

['And really large portions .']

[['portions', 'food style_options', 'positive', 'large']]

none

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']]'

generation

absa-quad

['Not cheap but very yummy .']

[['NULL', 'food prices', 'negative', 'Not cheap'], ['NULL', 'food quality', 'positive', 'yummy']]

none

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']]'

generation

absa-quad

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

[['NULL', 'ambience general', 'positive', 'intimate'], ['food', 'food quality', 'positive', 'superb'], ['NULL', 'restaurant general', 'positive', 'worth']]

none

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']]'

generation

absa-quad

['The whole set up is truly unprofessional and I wish Cafe Noir would get some good staff , because despite the current one this is a great place .']

[['staff', 'service general', 'negative', 'good'], ['Cafe Noir', 'restaurant general', 'positive', 'great']]

none

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']]'

generation

absa-quad

['Seating is always prompt , though the restaurant does fill up in the evening .']

[['Seating', 'service general', 'positive', 'prompt']]

none

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']]'

generation

absa-quad

['The DJ is awesome , I have been there for my birthday and a bunch of other times with friends and I keep going back .']

[['DJ', 'ambience general', 'positive', 'awesome'], ['NULL', 'restaurant general', 'positive', 'keep going back']]

none

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']]'

generation

absa-quad

['The anti-pasta was excellent , especially the calamari , as were the filling pasta mains .']

[['anti-pasta', 'food quality', 'positive', 'excellent'], ['calamari', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food quality', 'positive', 'excellent'], ['pasta mains', 'food style_options', 'positive', 'excellent']]

none

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']]'

generation

absa-quad

['A great choice at any cost and a great deal .']

[['NULL', 'restaurant general', 'positive', 'A great choice'], ['NULL', 'restaurant prices', 'positive', 'a great deal']]

none

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']]'

generation

absa-quad

['love the food .']

[['food', 'food quality', 'positive', 'love']]

none

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']]'

generation

absa-quad

['The reason there are 4 different results on citysearch for the same place is because they keep trying to start a new thread so they can stock it with positive reviews .']

[['NULL', 'restaurant miscellaneous', 'negative', 'start a new thread so they can stock it with positive reviews']]

none

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']]'

generation

absa-quad

['She just nodded and walked off .']

[['NULL', 'service general', 'negative', 'walked off']]

none

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']]'

generation

absa-quad

['Not only is the cuisine the best around , the service has always been attentive and charming .']

[['cuisine', 'food quality', 'positive', 'best'], ['service', 'service general', 'positive', 'attentive'], ['service', 'service general', 'positive', 'charming']]

none

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']]'

generation

absa-quad

['too large for just two people but nothing was left .']

[['NULL', 'food style_options', 'negative', 'too large'], ['NULL', 'food quality', 'positive', 'nothing was left']]

none

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']]'

generation

absa-quad

["We didn 't look like the other patrons in there so unfortunately I think that may have been part of the problem ."]

[['NULL', 'restaurant miscellaneous', 'negative', 'unfortunately']]

none

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']]'

generation

absa-quad

['If you are the type of person who likes being scared and entertained , this is a great place to go and eat .']

[['place', 'restaurant miscellaneous', 'positive', 'great']]

none

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']]'

generation

absa-quad

['The flavors are amazing and the value is phenomenal .']

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

none

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']]'

generation

absa-quad

['Ambiance relaxed and stylish .']

[['Ambiance', 'ambience general', 'positive', 'relaxed'], ['Ambiance', 'ambience general', 'positive', 'stylish']]

none

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']]'

generation

absa-quad

['I highly recommend visiting this restaurant and having dinner and drinks !']

[['restaurant', 'restaurant general', 'positive', 'recommend']]

none

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']]'

generation

absa-quad

['I had a huge group for my birthday and we were well taken care of .']

[['NULL', 'service general', 'positive', 'well taken care of']]

none

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']]'

generation

absa-quad

['Just go to Yamato and order the Red Dragon Roll .']

[['Yamato', 'restaurant general', 'positive', 'Just go']]

none

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']]'

generation

absa-quad

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

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

none

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']]'

generation

absa-quad

['The wine the service was very good too .']

[['wine', 'drinks quality', 'positive', 'good'], ['service', 'service general', 'positive', 'good']]

none

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']]'

generation

absa-quad

['Suan is a great place that I often take my friends ( classmates ) too .']

[['Suan', 'restaurant general', 'positive', 'great']]

none

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']]'

generation

absa-quad

['By far the best salad I have had in a fast food restaurant .']

[['salad', 'food quality', 'positive', 'best']]

none

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']]'

generation

absa-quad

['I would highly recommend it .']

[['NULL', 'restaurant general', 'positive', 'highly recommend']]

none

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']]'

generation

absa-quad

['Way below average']

[['NULL', 'restaurant general', 'negative', 'below average']]

none

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']]'

generation

absa-quad

['Ambience is so cute and quaint , good for business although we were there on vacation .']

[['Ambience', 'ambience general', 'positive', 'cute'], ['Ambience', 'ambience general', 'positive', 'quaint'], ['Ambience', 'ambience general', 'positive', 'good']]

none

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']]'

generation

absa-quad

['Food is great and inexpensive .']

[['Food', 'food quality', 'positive', 'great'], ['Food', 'food prices', 'positive', 'inexpensive']]

none

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']]'

generation

absa-quad

['Awsome Pizza especially the Margheritta slice .']

[['Pizza', 'food quality', 'positive', 'Awsome'], ['Margheritta slice', 'food quality', 'positive', 'Awsome']]

none

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']]'

generation

absa-quad

['( The asparagus , truffle oil , parmesan bruschetta is a winner ! )']

[['asparagus , truffle oil , parmesan bruschetta', 'food quality', 'positive', 'winner']]

none

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']]'

generation

absa-quad

['Considering you will spend at least $ 60 a head , I expect better service .']

[['service', 'service general', 'negative', 'expect better']]

none

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']]'

generation

absa-quad

['If you want good authentic Thai this place is not the place to go .']

[['Thai', 'food quality', 'negative', 'good authentic']]

none

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']]'

generation

absa-quad

['During the course of the past 3 months , the chef and staff changed and it was not for the better .']

[['chef', 'food quality', 'negative', 'changed'], ['staff', 'service general', 'negative', 'changed']]

none

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']]'

generation

absa-quad

['Try the congee and the donut like deep fried dough they call Ow Ley Soh , a delicious and sweet tasting bread .']

[['congee', 'food quality', 'positive', 'Try'], ['Ow Ley Soh', 'food quality', 'positive', 'delicious'], ['Ow Ley Soh', 'food quality', 'positive', 'sweet']]

none

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']]'

generation

absa-quad

['We could have made a meal of the yummy dumplings from the dumpling menu .']

[['dumplings', 'food quality', 'positive', 'yummy']]

none

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']]'

generation

absa-quad

['The setting is casual and romantic .']

[['setting', 'ambience general', 'positive', 'casual'], ['setting', 'ambience general', 'positive', 'romantic']]

none

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']]'

generation

absa-quad

['The sushi seemed pretty fresh and was adequately proportioned .']

[['sushi', 'food quality', 'positive', 'fresh'], ['sushi', 'food style_options', 'positive', 'proportioned']]

none

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']]'

generation

absa-quad

['A pleasant surprise .']

[['NULL', 'restaurant general', 'positive', 'pleasant surprise']]

none

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']]'

generation

absa-quad

['Great neighborhood joint .']

[['joint', 'restaurant general', 'positive', 'Great']]

none

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']]'

generation

absa-quad

["Food was amazing - I love Indian food and eat it quite regularly , but I can say this is one of the best I 've had ."]

[['Food', 'food quality', 'positive', 'amazing']]

none

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']]'

generation

absa-quad

["Though it 's been crowded most times I 've gone here , Bark always delivers on their food ."]

[['food', 'food quality', 'positive', 'delivers on their food'], ['Bark', 'restaurant miscellaneous', 'neutral', 'crowded']]

none

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']]'

generation

absa-quad

["It was n't as if this restaurant had any major bragging points before hand , but now it 's simply repulsive ."]

[['restaurant', 'restaurant general', 'negative', 'repulsive']]

none

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']]'

generation

absa-quad

['the bestt !']

[['NULL', 'restaurant general', 'positive', 'bestt']]

none

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']]'

generation

absa-quad

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

[['appetizers', 'food quality', 'positive', 'good']]

none

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']]'

generation

absa-quad

['Spreads and toppings are great - though a bit pricey .']

[['Spreads', 'food quality', 'positive', 'great'], ['toppings', 'food quality', 'positive', 'great'], ['Spreads', 'food prices', 'negative', 'pricey'], ['toppings', 'food prices', 'negative', 'pricey']]

none

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']]'

generation

absa-quad

["I 'm still mad that i had to pay for lousy food ."]

[['food', 'food quality', 'negative', 'lousy']]

none

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']]'

generation

absa-quad

['Right off the L in Brooklyn this is a nice cozy place with good pizza .']

[['pizza', 'food quality', 'positive', 'good'], ['place', 'ambience general', 'positive', 'nice cozy']]

none

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']]'

generation

absa-quad

['A beautifully designed dreamy Egyptian restaurant that gets sceney at night .']

[['Egyptian restaurant', 'ambience general', 'positive', 'dreamy']]

none

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']]'

generation

absa-quad

['Cheese plate is a varied delight and great bargain at $ 10 .']

[['Cheese plate', 'food quality', 'positive', 'varied delight'], ['Cheese plate', 'food style_options', 'positive', 'varied delight'], ['Cheese plate', 'food prices', 'positive', 'great bargain']]

none

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']]'

generation

absa-quad

['I was there for brunch recently , and we were tag teamed by a waitress and a waiter .']

[['waitress', 'service general', 'negative', 'tag teamed'], ['waiter', 'service general', 'negative', 'tag teamed']]

none

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']]'

generation

absa-quad

['Impressed ...']

[['NULL', 'restaurant general', 'positive', 'Impressed']]

none

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']]'

generation

absa-quad

["As BFC does n't take reservations you almost always have to wait by the bar - and be abused by the front of house staff until you are seated , which can be over an hour later !"]

[['BFC', 'restaurant miscellaneous', 'negative', 'wait'], ['front of house staff', 'service general', 'negative', 'abused'], ['NULL', 'service general', 'negative', 'over an hour later']]

none

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']]'

generation

absa-quad

['And it was quick which is very important .']

[['NULL', 'service general', 'positive', 'quick']]

none

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']]'

generation

absa-quad

['Stay away']

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

none

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']]'

generation

absa-quad

['The flavors robust and subtle .']

[['NULL', 'food quality', 'positive', 'robust'], ['NULL', 'food quality', 'positive', 'subtle']]

none

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']]'

generation

absa-quad

['Just not good at all .']

[['NULL', 'food quality', 'negative', 'not good']]

none

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']]'

generation

absa-quad

['Great spot , whether looking for a couple of drinks or quiet dinner .']

[['spot', 'restaurant general', 'positive', 'Great'], ['spot', 'restaurant miscellaneous', 'positive', 'Great']]

none

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']]'

generation

absa-quad

['The drinks are great , especially when made by Raymond .']

[['drinks', 'drinks quality', 'positive', 'great'], ['Raymond', 'service general', 'positive', 'great']]

none

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']]'

generation

absa-quad

["The dishes offered were unique , very tasty and fresh from the lamb sausages , sardines with biscuits , large whole shrimp to the amazing pistachio ice cream ( the best and freshest I 've ever had ) ."]

[['dishes', 'food quality', 'positive', 'unique'], ['dishes', 'food quality', 'positive', 'tasty'], ['dishes', 'food quality', 'positive', 'fresh'], ['pistachio ice cream', 'food quality', 'positive', 'amazing']]

none

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']]'

generation

absa-quad

['After all that , they complained to me about the small tip .']

[['NULL', 'service general', 'negative', 'complained']]

none

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']]'

generation

absa-quad

['Give it a try and enjoy .']

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

none

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']]'

generation

absa-quad

["When the bill came , nothing was comped , so I told the manager very politely that we were willing to pay for the wine , but I didn 't think I should have to pay for food with a maggot in it ."]

[['NULL', 'service general', 'negative', 'maggot']]

none

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']]'