{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:02:41Z","timestamp":1760709761266,"version":"3.41.0"},"reference-count":46,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T00:00:00Z","timestamp":1542585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2017YFB1002103"],"award-info":[{"award-number":["2017YFB1002103"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2019,3,31]]},"abstract":"<jats:p>Document-level sentiment classification aims to predict a user\u2019s sentiment polarity in a document about a product. Most existing methods only focus on review contents and ignore users who post reviews. In fact, when reviewing a product, different users have different word-using habits to express opinions (i.e., word-level user preference), care about different attributes of the product (i.e., aspect-level user preference), and have different characteristics to score the review (i.e., polarity-level user preference). These preferences have great influence on interpreting the sentiment of text. To address this issue, we propose a model called Hierarchical User Attention Network (HUAN), which incorporates multi-level user preference into a hierarchical neural network to perform document-level sentiment classification. Specifically, HUAN encodes different kinds of information (word, sentence, aspect, and document) in a hierarchical structure and imports user embedding and user attention mechanism to model these preferences. Empirical results on two real-world datasets show that HUAN achieves state-of-the-art performance. Furthermore, HUAN can also mine important attributes of products for different users.<\/jats:p>","DOI":"10.1145\/3234512","type":"journal-article","created":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T14:19:23Z","timestamp":1542723563000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Incorporating Multi-Level User Preference into Document-Level Sentiment Classification"],"prefix":"10.1145","volume":"18","author":[{"given":"Junjie","family":"Li","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomian","family":"Kang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer, Central China Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, University of Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,11,19]]},"reference":[{"volume-title":"Proceedings of the 2015 Annual Meeting of the Association for Computational Linguistics. 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