Papers › Capturing User and Product Information for Document Level Sentiment Analysis with Deep...
Capturing User and Product Information for Document Level Sentiment Analysis with Deep Memory Network
Zi-Yi Dou
Document-level sentiment classification is a fundamental problem which aims to predict a user{'}s overall sentiment about a product in a document. Several methods have been proposed to tackle the problem whereas most of them fail to consider the influence of users who express the sentiment and products which are evaluated. To address the issue, we propose a deep memory network for document-level sentiment classification which could capture the user and product information at the same time. To prove the effectiveness of our algorithm, we conduct experiments on IMDB and Yelp datasets and the results indicate that our model can achieve better performance than several existing methods.
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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 | User and product information | UPDMN | IMDB (Acc) | 46.5 | #8 of 10 | Archive leaderboard | report |
| Sentiment Analysis | User and product information | UPDMN | Yelp 2013 (Acc) | 63.9 | #8 of 10 | Archive leaderboard | report |
| Sentiment Analysis | User and product information | UPDMN | Yelp 2014 (Acc) | 61.3 | #8 of 10 | 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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