{"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/dual-memory-network-model-for-biased-product","title":"Dual Memory Network Model for Biased Product Review Classification","arxiv_id":"1809.05807","date":"2018-09-16","proceeding":"WS 2018 10","authors":["Yunfei Long","Mingyu Ma","Qin Lu","Rong Xiang","Chu-Ren Huang"],"abstract":"In sentiment analysis (SA) of product reviews, both user and product\ninformation are proven to be useful. Current tasks handle user profile and\nproduct information in a unified model which may not be able to learn salient\nfeatures of users and products effectively. In this work, we propose a dual\nuser and product memory network (DUPMN) model to learn user profiles and\nproduct reviews using separate memory networks. Then, the two representations\nare used jointly for sentiment prediction. The use of separate models aims to\ncapture user profiles and product information more effectively. Compared to\nstate-of-the-art unified prediction models, the evaluations on three benchmark\ndatasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives\nperformance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are\nalso deemed very significant measured by p-values.","url_abs":"http://arxiv.org/abs/1809.05807v1","url_pdf":"http://arxiv.org/pdf/1809.05807v1.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":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"memory-network","method_name":"Memory Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-user-and-product","task":"Sentiment Analysis","dataset":"User and product information","model":"DUPMN","rank_in_archive_order":6,"of":10,"metrics":{"IMDB (Acc)":"53.9","Yelp 2013 (Acc)":"66.2","Yelp 2014 (Acc)":"67.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}