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However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification.<\/jats:p>","DOI":"10.3233\/ida-194952","type":"journal-article","created":{"date-parts":[[2021,2,2]],"date-time":"2021-02-02T19:44:33Z","timestamp":1612295073000},"page":"205-223","source":"Crossref","is-referenced-by-count":3,"title":["Hierarchical features-based targeted aspect extraction from online reviews"],"prefix":"10.1177","volume":"25","author":[{"given":"Jin","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Ministry of Education, Hefei, Anhui, China"},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Ministry of Education, Hefei, Anhui, China"},{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University, Sydney, Australia"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"MiningLamp Academy of Sciences, Mininglamp Techonologies, Beijing, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDA-194952_ref1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s10115-017-1072-y","article-title":"Learning patterns for discovering domain-oriented opinion words","volume":"55","author":"Agathangelou","year":"2018","journal-title":"Knowledge and Information Systems"},{"doi-asserted-by":"crossref","unstructured":"A. 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