Abstract
Public opinion monitoring refers to real-time first story detection (FSD) on a particular Internet news event. It play an important part in finding news propagation tendency. Current opinion monitoring methods are related to text matching. However, it has some limitations such as latent and hidden topic discovery and incorrect relevance ranking of matching results on large-scale data. In this paper, we propose one improved solution to live public opinion monitoring: stream-based live probabilistic topic computing and matching. Our method attempts to address the disadvantages such as semantic matching and low efficiency on timely big data. Topic real-time computing with stream processing paradigm and topic matching with query-time document and field boosting are proposed to make substantial improvements. Finally, our experimental evaluation on topic computing and matching using crawled historical Netease news records shows the high effectiveness and efficiency of the proposed approach.
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Acknowledgments
This work was supported by the Science and Technology Program of University of Jinan (XKY1734), the Open Project Joint Funding of Information Science and Engineering School of Linyi University and Discipline Team of Intelligent Logistics and Information Engineering (LDXX2017KF155), the Shandong Provincial Natural Science Foundation (ZR201702170261), the Shandong Provincial Key R&D Program (2015GGX106007 & 2016ZDJS01A12), and the Project of Shandong Province Higher Educational Science and Technology Program (J16LN13).
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Ma, K., Yu, Z., Ji, K., Yang, B. (2017). Stream-Based Live Probabilistic Topic Computing and Matching. In: Ibrahim, S., Choo, KK., Yan, Z., Pedrycz, W. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2017. Lecture Notes in Computer Science(), vol 10393. Springer, Cham. https://doi.org/10.1007/978-3-319-65482-9_27
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DOI: https://doi.org/10.1007/978-3-319-65482-9_27
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