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Two experiments are described in this paper: the first focuses on testing the performance of the Word2Vec model on extracting product aspect words, the second addresses how well the extracted features obtained are recognizable by human cognition. A new metric called the \u201csplit value\u201d that is based on cluster similarity and diversity is introduced to examine the consistency of clustering algorithm. The authors' experiments suggest that meaningful clusters, which provide insights to the product attributes and sentiments, could be extracted from the reviews.<\/jats:p>","DOI":"10.4018\/ijssci.2018040101","type":"journal-article","created":{"date-parts":[[2018,3,22]],"date-time":"2018-03-22T13:59:44Z","timestamp":1521727184000},"page":"1-24","source":"Crossref","is-referenced-by-count":2,"title":["Discovering Attribute-Specific Features From Online Reviews"],"prefix":"10.4018","volume":"10","author":[{"given":"Xiaonan","family":"Jing","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, USA"}]},{"given":"Penghao","family":"Wang","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, USA"}]},{"given":"Julia M.","family":"Rayz","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, USA"}]}],"member":"2432","reference":[{"key":"IJSSCI.2018040101-0","doi-asserted-by":"publisher","DOI":"10.1177\/1461444812445878"},{"key":"IJSSCI.2018040101-1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1082"},{"key":"IJSSCI.2018040101-2","doi-asserted-by":"publisher","DOI":"10.1145\/775152.775226"},{"key":"IJSSCI.2018040101-3","doi-asserted-by":"publisher","DOI":"10.17722\/jorm.v2i2.46"},{"key":"IJSSCI.2018040101-4","doi-asserted-by":"publisher","DOI":"10.3115\/1219840.1219885"},{"key":"IJSSCI.2018040101-5","unstructured":"Firth, J. 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