Papers › Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
Chi Sun, Luyao Huang, Xipeng Qiu
Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets.
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Code
Syntology Ran 8 of 27 code samples harvested from 3 repositories linked to this paper; 19 have no recorded run. Of those that ran: 3 ran · honoured contract; 3 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
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Code Syntology ran Syntology
27 samples harvested; 8 ran; 3 honoured the contract we drafted; 19 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect Category Detection | SemEval 2014 Task 4 Subtask 3 | BERT-pair-NLI-B | F1 score | 92.18 | #1 of 1 | Archive leaderboard | report |
| Aspect Category Detection | SemEval 2014 Task 4 Subtask 3 | BERT-pair-NLI-B | Precision | 93.57 | #1 of 1 | Archive leaderboard | report |
| Aspect Category Detection | SemEval 2014 Task 4 Subtask 3 | BERT-pair-NLI-B | Recall | 90.83 | #1 of 1 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Subtask 4 | BERT-pair-QA-B | Accuracy (3-way) | 89.9 | #1 of 2 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Subtask 4 | BERT-pair-QA-B | Accuracy (4-way) | 85.9 | #1 of 2 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Subtask 4 | BERT-pair-QA-B | Binary Accuracy | 95.6 | #1 of 2 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | Sentihood | BERT-pair-QA-B | Aspect | 87.9 | #1 of 5 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | Sentihood | BERT-pair-QA-B | Sentiment | 93.3 | #1 of 5 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | Sentihood | BERT-pair-QA-M | Aspect | 86.4 | #2 of 5 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | Sentihood | BERT-pair-QA-M | Sentiment | 93.6 | #2 of 5 | 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.
Methods
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