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We hypothesize that nonemotionally charged descriptions can be applied to predict those opinions. The descriptions may include indicators of product size (<jats:italic>tall<\/jats:italic>), commonplace (<jats:italic>some<\/jats:italic>), frequency of happening (<jats:italic>often<\/jats:italic>), and reviewer certainty (<jats:italic>maybe<\/jats:italic>). We first construct patterns of how the descriptions are used in consumer-written texts and then represent individual reviews through these patterns. We propose a semantic hierarchy that organizes individual words into opinion types. We run machine learning algorithms on five data sets of user-written product reviews: four are used in classification experiments, another one for regression and classification. The obtained results support the use of non-emotional descriptions in opinion learning.<\/jats:p>","DOI":"10.1017\/s135132491100012x","type":"journal-article","created":{"date-parts":[[2011,3,11]],"date-time":"2011-03-11T12:55:23Z","timestamp":1299848123000},"page":"541-567","source":"Crossref","is-referenced-by-count":5,"title":["Learning opinions in user-generated web content"],"prefix":"10.1017","volume":"17","author":[{"given":"M.","family":"SOKOLOVA","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"LAPALME","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2011,3,11]]},"reference":[{"key":"S135132491100012X_ref68","doi-asserted-by":"crossref","unstructured":"Yu H. , and Hatzivassiloglou V. 2003. Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. 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