Papers › FABSA: An aspect-based sentiment analysis dataset of user reviews
FABSA: An aspect-based sentiment analysis dataset of user reviews
Georgios Kontonatsios, Jordan Clive, Georgia Harrison, Thomas Metcalfe, Patrycja Sliwiak, Hassan Tahir, Aji Ghose
Aspect-based sentiment analysis (ABSA) aims at automatically extracting aspects of entities and classifying the polarity of each extracted aspect. The majority of available ABSA systems heavily rely on manually annotated datasets to train supervised machine learning models. However, the development of such manually curated datasets is a labour-intensive process and therefore existing ABSA datasets cover only a few domains and they are limited in size. In response, we present FABSA (Feedback ABSA), a new large-scale and multi-domain ABSA dataset of feedback reviews. FABSA consists of approximately 10,500 reviews which span across 10 domains. We conduct a number of experiments to evaluate the performance of state-of-the-art deep learning models when applied to the FABSA dataset. Our results demonstrate that ABSA models can generalise across different domains when trained on our FABSA dataset while the performance of the models is enhanced when using a larger training dataset. Our FABSA dataset is publicly available.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect Category Sentiment Analysis | FABSA | DeBERTa-pair-large | F1 (%) | 80.9 | #1 of 4 | Archive leaderboard | report |
| Aspect Category Sentiment Analysis | FABSA | RoBERTa-pair-large | F1 (%) | 80.0 | #2 of 4 | Archive leaderboard | report |
| Aspect Category Sentiment Analysis | FABSA | BERT-single-large | F1 (%) | 78.8 | #3 of 4 | Archive leaderboard | report |
| Aspect Category Sentiment Analysis | FABSA | BERT-PT | F1 (%) | 78.8 | #4 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | FABSA | DeBERTa-pair-large | F1 (%) | 80.9 | #1 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | FABSA | RoBERTa-pair-large | F1 (%) | 80.0 | #2 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | FABSA | BERT-PT | F1 (%) | 78.8 | #3 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | FABSA | BERT-single-large | F1 (%) | 78.8 | #4 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections