Papers › Learning Latent Opinions for Aspect-Level Sentiment Classification
Learning Latent Opinions for Aspect-Level Sentiment Classification
Bailin Wang, Wei Lu
Aspect-level sentiment classification aims at detecting the sentiment expressed towards a particular target in a sentence. Based on the observation that the sentiment polarity is often related to specific spans in the given sentence, it is possible to make use of such information for better classification. On the other hand, such information can also serve as justifications associated with the predictions.We propose a segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer. The model simulates human's process of inferring sentiment information when reading: when given a target, humans tend to search for surrounding relevant text spans in the sentence before making an informed decision on the underlying sentiment information.We perform sentiment classification tasks on publicly available datasets on online reviews across different languages from SemEval tasks and social comments from Twitter. Extensive experiments show that our model achieves the state-of-the-art performance while extracting interpretable sentiment expressions.
Code
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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 | SA-LSTM-P | Laptop (Acc) | 75.1 | #25 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | SA-LSTM-P | Mean Acc (Restaurant + Laptop) | 78.35 | #25 of 48 | Archive leaderboard | report |
| Aspect-Based Sentiment Analysis (ABSA) | SemEval-2014 Task-4 | SA-LSTM-P | Restaurant (Acc) | 81.6 | #25 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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