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- W3048777071 abstract "Web-based reading services such as Google News and Yahoo! News have become increasingly popular with the growth of news information services on the Internet. To help users cope with the information overload on these search engines, recommender systems and personalization techniques are proposed. Acting as a kind of algorithmic curators, these services help users find content that matches their personal interests and tastes using their browser history and past behavior as a basis for recommendations. However, several researchers have criticized recommender systems, arguing that overspecialized recommendations lead to the creation of isolated communities (Van Alstyne and Brynjolfsson, 2005), the emergence of extremist opinions (Mutz and Young, 2011) and the overall degradation of the public sphere (Parisier, 2011; Sustein, 2007). The aim of our research to propose an opinion mining method to classify divergent opinions from a controversial debate on the press. We want to contribute to a solution to diversify recommendations in web-based reading services. Our classification approach advocates for the study of linguistics aspects of corpus prior to the classification task, to orientate the selection of the textual criteria which may contribute to the application performance. This approach explores theoretical concepts from Interpretative Semantics formulated by Francois Rastier and uses textometric techniques for the corpora analyses. Our corpora are composed by opinionated articles about the 2012 student protest in Quebec against the raise of the tuition fees announced by the Liberal Premier Jean Charest." @default.
- W3048777071 created "2020-08-18" @default.
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- W3048777071 date "2017-01-01" @default.
- W3048777071 modified "2023-10-03" @default.
- W3048777071 title "News recommendation based on opinion mining: an approach to assist the automatic classification of controversies" @default.
- W3048777071 doi "https://doi.org/10.22215/datapower.v2017i0.130" @default.
- W3048777071 hasPublicationYear "2017" @default.
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