Papers › Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence

22 Mar 2019NAACL 2019 6arXiv:1903.09588archive 2025-07-28

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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HSLCY/ABSA-BERT-pair officialmentioned in paperpytorchMIT report
LorenzoAgnolucci/BERT_for_ABSA mentioned on GitHubpytorch report
anshulwadhawan/ABSA mentioned on GitHubpytorch report
atharvajdhumal/Sentiment-Analysis mentioned on GitHubpytorch report
mwbrulhardt/yelp-absa mentioned on GitHubpytorchMIT report
recommeddit/labs mentioned on GitHubpytorchMIT report
ywu94/Code-Notes mentioned on GitHubpytorchMIT report

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3ran · honoured contract
3ran · our draft was wrong
1ran · fixture could not drive it
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convert_to_unicode HSLCY/ABSA-BERT-pair/tokenization.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 1923fc05163d207d · report
gelu HSLCY/ABSA-BERT-pair/modeling.py official repository ran · honoured contract fingerprinted MIT (permissive) · 40e9fee2e0b7e278 · report
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Tasks

Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)General ClassificationNatural Language InferenceQuestion AnsweringSentenceSentence-Pair ClassificationSentiment Analysis

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

AdamAttentionAttention DropoutBERTDense ConnectionsDropoutLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPiece

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