Abstract
Location-based topic modeling is an emerging domain to capture topical trends over the area dimension, which makes it possible to conduct further analysis on the various preference of users in different areas, such as investigating users’ social opinion tendencies, generating personalized recommendation and so on. In this paper, we proposed a novel topic model called Area-LDA to discover the latent area-specific topic. Previous works which based on pre-discretized or post-hoc analysis of area topics had no ability to help generate topic for a document. Different from previous works, our model extends the original LDA by associating each document with area factor and introducing a new area distribution over topics into the model. Therefore, for each generated document, the distribution over topics is influenced by both word co-occurrences of the document and word co-occurrences of the corresponding area. We present the experimental analysis over the real-world dataset and the results demonstrate the effectiveness of the proposed method to mine interpretable topic trends on different areas.
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Guo, H., Zhang, L., Li, Z. (2018). Area Topic Model. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2017. Advances in Intelligent Systems and Computing, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-69096-4_22
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DOI: https://doi.org/10.1007/978-3-319-69096-4_22
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