FABSA (An aspect-based sentiment analysis dataset of Customer Feedback reviews)

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, 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.

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 or SentiHood).

This dataset is part of the FABSA paper, 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).

Academic Paper

@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}, }