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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: int64 |
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- name: org_index |
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dtype: int64 |
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- name: data_source |
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dtype: string |
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- name: industry |
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dtype: string |
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- name: text |
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dtype: string |
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- name: labels |
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sequence: |
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sequence: string |
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- name: label_codes |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2599501.8469831664 |
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num_examples: 7930 |
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- name: validation |
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num_bytes: 346490.977586533 |
|
num_examples: 1057 |
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- name: test |
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num_bytes: 520228.17543030076 |
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num_examples: 1587 |
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download_size: 1010316 |
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dataset_size: 3466221.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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|
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# FABSA, An aspect-based sentiment analysis dataset in the Customer Feedback space (Trustpilot, Google Play and Apple Store reviews). |
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A professionally annotated dataset released by [Chattermill AI](https://chattermill.com/), with 8 years of experience in leveraging advanced ML analytics in the customer feedback space for high-profile clients such as Amazon and Uber. |
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Two annotators possess extensive experience in developing human-labeled ABSA datasets for commercial companies, while the third annotator holds a PhD in computational linguistics. |
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## Task |
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This dataset encompasses **Aspect Category Sentiment Analysis** and is suitable for both **Aspect Category Detection** (ACD) and **Aspect Category Sentiment Classification** (ACSC). ACD in sentiment analysis identifies aspect categories mentioned in a sentence. These categories are conceptual; they may not explicitly appear in the review and are chosen from a predefined list of Aspect Categories. ACSC classifies the sentiment polarities of these conceptual aspect categories. |
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The predefined list of Aspect Categories for this dataset are: |
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``` |
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category category_code |
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0 Account management: Account access account-management.account-access |
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1 Company brand: Competitor company-brand.competitor |
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2 Company brand: General satisfaction company-brand.general-satisfaction |
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3 Company brand: Reviews company-brand.reviews |
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4 Logistics rides: Speed logistics-rides.speed |
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5 Online experience: App website online-experience.app-website |
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6 Purchase booking experience: Ease of use purchase-booking-experience.ease-of-use |
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7 Staff support: Attitude of staff staff-support.attitude-of-staff |
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8 Staff support: Email staff-support.email |
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9 Staff support: Phone staff-support.phone |
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10 Value: Discounts promotions value.discounts-promotions |
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11 Value: Price value for money value.price-value-for-money |
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``` |
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## Annotation Scheme |
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The FABSA dataset is manually labeled according to a hierarchical annotation scheme that includes parent and child aspect categories. Each aspect category comes with an associated sentiment label (positive, negative, and neutral), creating a total of (12 × 3) target classification categories. |
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In line with prior studies, we employ a multi-label classification scheme where each review is tagged with one or more aspect + sentiment labels. Thus, a single review can cover multiple aspects and express various (sometimes contrasting) polarities. For example, |
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``` |
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Customer Review: |
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“product is very good but customer service is really bad, they never respond” |
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Labels: [Company brand: General satisfaction, positive), (Staff support: Attitude of staff, Negative)" |
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``` |
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 |
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## Release |
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There has been a lack of high-quality ABSA datasets covering broad domains and addressing real-world applications. Academic progress has been confined to benchmarking on domain-specific, toy datasets such as restaurants and laptops, which are limited in size (e.g., [SemEval Task ABSA](https://aclanthology.org/S16-1002.pdf) or [SentiHood](https://aclanthology.org/C16-1146/)). |
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This dataset is part of the [FABSA paper](https://www.sciencedirect.com/science/article/pii/S0925231223009906), and we release it hoping to advance academic progress as tools for ingesting and analyzing customer feedback at scale improve significantly, yet evaluation datasets continue to lag. FABSA is a new, large-scale, multi-domain ABSA dataset of feedback reviews, consisting of approximately 10,500 reviews spanning 10 domains (Fashion, Consulting, Travel Booking, Ride-hailing, Banking, Trading, Streaming, Price Comparison, Information Technology, and Groceries). |
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## Citation |
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``` |
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@article{KONTONATSIOS2023126867, |
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title = {FABSA: An aspect-based sentiment analysis dataset of user reviews}, |
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journal = {Neurocomputing}, |
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volume = {562}, |
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pages = {126867}, |
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year = {2023}, |
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issn = {0925-2312}, |
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doi = {https://doi.org/10.1016/j.neucom.2023.126867}, |
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url = {https://www.sciencedirect.com/science/article/pii/S0925231223009906}, |
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author = {Georgios Kontonatsios and Jordan Clive and Georgia Harrison and Thomas Metcalfe and Patrycja Sliwiak and Hassan Tahir and Aji Ghose}, |
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keywords = {ABSA, Multi-domain dataset, Deep learning}, |
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} |
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``` |
