Abstract
This paper proposes a quality topic extraction on Twitter based on author’s role on bipartite networks. We suppose that author’s role which means who were in what group, affects the quality of extracted topics. Our proposed method expresses relations between authors and words as bipartite networks, explores author’s role by forming clusters using our original community detection technique, and finds quality topics considering the semantic accuracy of words and author’s role.
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Acknowledgment
This paper was supported by the Grant-in-Aid for Scientific Research (KAKENHI Grant Numbers 26280090, 15K00314, and 17H00762) from the Japan Society for the Promotion of Science.
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Hashimoto, T., Kuboyama, T., Okamoto, H., Shin, K. (2017). Topic Extraction on Twitter Considering Author’s Role Based on Bipartite Networks. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_17
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DOI: https://doi.org/10.1007/978-3-319-67786-6_17
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