Papers › A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu
Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation subspaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | MR | MEAN | Accuracy | 84.5 | #5 of 19 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | MEAN | Accuracy | 51.4 | #16 of 31 | 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.
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