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.
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Liu, F., Wu, N. (2016). SentiLDA — An Effective and Scalable Approach to Mine Opinions of Consumer Reviews by Utilizing Both Structured and Unstructured Data. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_6
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DOI: https://doi.org/10.1007/978-3-319-43946-4_6
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