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• W2482202261 abstract "With the help of Internet and Web technologies, more and more consumers tend to seek opinions online before making purchase decisions. However, with the ever-increasing volume of user generated reviews, people are overwhelmed with the amount of data they have. Thus there is a great need for a system that can summarize the reviews and produce a set of aspects being mentioned in the reviews together with the pros/cons being expressed to them. To address the need, this paper proposes a new probabilistic topic model, SentiLDA, for mining reviews (unstructured data) and their ratings (structured data) jointly to detect the product/service aspects and their corresponding positive and negative opinions simultaneously. A key feature of SentiLDA is that it is capable of mining positive and negative sub-topics under the same aspect without the need of sentiment seed words. Experiment results show that the performance of SentiLDA outperforms the other related state-of-the-art models in detecting product/service aspects and their corresponding sentiments in reviews." @default.
• W2482202261 created "2016-08-23" @default.
• W2482202261 creator A5015110457 @default.
• W2482202261 creator A5033073898 @default.
• W2482202261 date "2016-01-01" @default.
• W2482202261 modified "2023-09-22" @default.
• W2482202261 title "SentiLDA — An Effective and Scalable Approach to Mine Opinions of Consumer Reviews by Utilizing Both Structured and Unstructured Data" @default.
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• W2482202261 doi "https://doi.org/10.1007/978-3-319-43946-4_6" @default.
• W2482202261 hasPublicationYear "2016" @default.
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