--- dataset_info: features: - name: id dtype: int64 - name: org_index dtype: int64 - name: data_source dtype: string - name: industry dtype: string - name: text dtype: string - name: labels sequence: sequence: string - name: label_codes dtype: string splits: - name: train num_bytes: 2599501.8469831664 num_examples: 7930 - name: validation num_bytes: 346490.977586533 num_examples: 1057 - name: test num_bytes: 520228.17543030076 num_examples: 1587 download_size: 1010316 dataset_size: 3466221.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* --- # FABSA, An aspect-based sentiment analysis dataset in the Customer Feedback space (Trustpilot, Google Play and Apple Store reviews). 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. Two annotators possess extensive experience in developing human-labeled ABSA datasets for commercial companies, while the third annotator holds a PhD in computational linguistics. ## Task 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. The predefined list of Aspect Categories for this dataset are: ``` category category_code 0 Account management: Account access account-management.account-access 1 Company brand: Competitor company-brand.competitor 2 Company brand: General satisfaction company-brand.general-satisfaction 3 Company brand: Reviews company-brand.reviews 4 Logistics rides: Speed logistics-rides.speed 5 Online experience: App website online-experience.app-website 6 Purchase booking experience: Ease of use purchase-booking-experience.ease-of-use 7 Staff support: Attitude of staff staff-support.attitude-of-staff 8 Staff support: Email staff-support.email 9 Staff support: Phone staff-support.phone 10 Value: Discounts promotions value.discounts-promotions 11 Value: Price value for money value.price-value-for-money ``` ## Annotation Scheme 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. 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, ``` Customer Review: “product is very good but customer service is really bad, they never respond” Labels: [Company brand: General satisfaction, positive), (Staff support: Attitude of staff, Negative)" ``` ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/608995d619137b3a6ba76113/Lr60Oc6NGalkOEj-_Gtzk.jpeg) ## Release 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/)). 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). ## Citation ``` @article{KONTONATSIOS2023126867, title = {FABSA: An aspect-based sentiment analysis dataset of user reviews}, journal = {Neurocomputing}, volume = {562}, pages = {126867}, year = {2023}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2023.126867}, url = {https://www.sciencedirect.com/science/article/pii/S0925231223009906}, author = {Georgios Kontonatsios and Jordan Clive and Georgia Harrison and Thomas Metcalfe and Patrycja Sliwiak and Hassan Tahir and Aji Ghose}, keywords = {ABSA, Multi-domain dataset, Deep learning}, } ```