Abstract
The word of mouth information over certain review sites affects various activities from person to person. In large-scale review sites, it can happen that evaluation tendency of a product changes in a large way by only a few reviews that were rated and posted by certain users. Thus, it is very important to be able to detect those influential reviews in social media analysis. We propose an algorithm that can efficiently divide stream data of review scores by maximizing the likelihood of generating the observed sequence data. We assume that the user’s fundamental scoring behavior follows a multinomial distribution model and formulate a division problem.
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Yamagishi, Y., Okubo, S., Saito, K., Ohara, K., Kimura, M., Motoda, H. (2014). A Method to Divide Stream Data of Scores over Review Sites. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_78
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DOI: https://doi.org/10.1007/978-3-319-13560-1_78
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
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