{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:15:54Z","timestamp":1750306554558,"version":"3.41.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2015,3,31]],"date-time":"2015-03-31T00:00:00Z","timestamp":1427760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Singapore Ministry of Education's Academic Research Fund Tier 2","award":["(ARC30\/12)"],"award-info":[{"award-number":["(ARC30\/12)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2015,5,4]]},"abstract":"<jats:p>\n            Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an\n            <jats:italic>association-based bootstrapping<\/jats:italic>\n            method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models,\n            <jats:italic>likelihood ratio tests<\/jats:italic>\n            (LRTs) and\n            <jats:italic>latent semantic analysis<\/jats:italic>\n            (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.\n          <\/jats:p>","DOI":"10.1145\/2663359","type":"journal-article","created":{"date-parts":[[2015,4,3]],"date-time":"2015-04-03T20:29:44Z","timestamp":1428092984000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["An Association-Based Unified Framework for Mining Features and Opinion Words"],"prefix":"10.1145","volume":"6","author":[{"given":"Zhen","family":"Hai","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuiyu","family":"Chang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gao","family":"Cong","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher C.","family":"Yang","sequence":"additional","affiliation":[{"name":"Drexel University, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2015,3,31]]},"reference":[{"volume-title":"Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL\u201909)","author":"Agarwal Apoorv","key":"e_1_2_1_1_1","unstructured":"Apoorv Agarwal , Fadi Biadsy , and Kathleen R. Mckeown . 2009. Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams . In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL\u201909) . 24--32. Apoorv Agarwal, Fadi Biadsy, and Kathleen R. Mckeown. 2009. Contextual phrase-level polarity analysis using lexical affect scoring and syntactic N-grams. In Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL\u201909). 24--32."},{"key":"e_1_2_1_2_1","article-title":"Latent Dirichlet allocation","author":"Blei David M.","year":"2003","unstructured":"David M. Blei , Andrew Y. Ng , and Michael I. Jordan . 2003 . Latent Dirichlet allocation . Journal of Machine Learning Research 3 ( March 2003), 993--1022. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. 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