Papers › BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions.~We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective. The datasets and code are available at https://www.cs.uic.edu/~hxu/.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Sub Task 1 | BERT-PT | Laptop (F1) | 84.26 | #2 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval 2014 Task 4 Sub Task 1 | BERT-PT | Restaurant (F1) | 77.97 | #2 of 4 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | BERT-PT | Laptop (Acc) | 78.07 | #17 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | BERT-PT | Mean Acc (Restaurant + Laptop) | 81.51 | #17 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | BERT-PT | Restaurant (Acc) | 84.95 | #17 of 48 | 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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