{"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/cold-start-aware-user-and-product-attention","title":"Cold-Start Aware User and Product Attention for Sentiment Classification","arxiv_id":"1806.05507","date":"2018-06-14","proceeding":"ACL 2018 7","authors":["Reinald Kim Amplayo","Jihyeok Kim","Sua Sung","Seung-won Hwang"],"abstract":"The use of user/product information in sentiment analysis is important,\nespecially for cold-start users/products, whose number of reviews are very\nlimited. However, current models do not deal with the cold-start problem which\nis typical in review websites. In this paper, we present Hybrid Contextualized\nSentiment Classifier (HCSC), which contains two modules: (1) a fast word\nencoder that returns word vectors embedded with short and long range dependency\nfeatures; and (2) Cold-Start Aware Attention (CSAA), an attention mechanism\nthat considers the existence of cold-start problem when attentively pooling the\nencoded word vectors. HCSC introduces shared vectors that are constructed from\nsimilar users/products, and are used when the original distinct vectors do not\nhave sufficient information (i.e. cold-start). This is decided by a\nfrequency-guided selective gate vector. Our experiments show that in terms of\nRMSE, HCSC performs significantly better when compared with on famous datasets,\ndespite having less complexity, and thus can be trained much faster. More\nimportantly, our model performs significantly better than previous models when\nthe training data is sparse and has cold-start problems.","url_abs":"http://arxiv.org/abs/1806.05507v1","url_pdf":"http://arxiv.org/pdf/1806.05507v1.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":"cold-start-aware-user-and-product-attention","repo_url":"https://github.com/rktamplayo/HCSC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"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":"HCSC","rank_in_archive_order":4,"of":10,"metrics":{"IMDB (Acc)":"54.2","Yelp 2013 (Acc)":"65.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.05507","atlas_url":"https://app.syntology.ai/?focus=1806.05507","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}