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The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.<\/jats:p>","DOI":"10.1177\/0165551519845854","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T04:06:39Z","timestamp":1556510799000},"page":"406-418","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":40,"title":["Concept-LDA: Incorporating Babelfy into LDA for aspect extraction"],"prefix":"10.1177","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0658-592X","authenticated-orcid":false,"given":"Ekin","family":"Ekinci","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Kocaeli University, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sevin\u00e7","family":"\u0130lhan Omurca","sequence":"additional","affiliation":[{"name":"Department of Computer 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