{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-review-representations-with-user","title":"Improving Review Representations with User Attention and Product Attention for Sentiment Classification","arxiv_id":"1801.07861","date":"2018-01-24","proceeding":null,"authors":["Zhen Wu","Xin-yu Dai","Cunyan Yin","Shu-Jian Huang","Jia-Jun Chen"],"abstract":"Neural network methods have achieved great success in reviews sentiment\nclassification. Recently, some works achieved improvement by incorporating user\nand product information to generate a review representation. However, in\nreviews, we observe that some words or sentences show strong user's preference,\nand some others tend to indicate product's characteristic. The two kinds of\ninformation play different roles in determining the sentiment label of a\nreview. Therefore, it is not reasonable to encode user and product information\ntogether into one representation. In this paper, we propose a novel framework\nto encode user and product information. Firstly, we apply two individual\nhierarchical neural networks to generate two representations, with user\nattention or with product attention. Then, we design a combined strategy to\nmake full use of the two representations for training and final prediction. The\nexperimental results show that our model obviously outperforms other\nstate-of-the-art methods on IMDB and Yelp datasets. Through the visualization\nof attention over words related to user or product, we validate our observation\nmentioned above.","url_abs":"http://arxiv.org/abs/1801.07861v1","url_pdf":"http://arxiv.org/pdf/1801.07861v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-review-representations-with-user","repo_url":"https://github.com/wuzhen247/HUAPA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-user-and-product","task":"Sentiment Analysis","dataset":"User and product information","model":"HUAPA","rank_in_archive_order":3,"of":10,"metrics":{"IMDB (Acc)":"55.0","Yelp 2013 (Acc)":"68.3","Yelp 2014 (Acc)":"68.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.07861","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}