Abstract
BBS, often referred to as forum, is a system that offers so much information, where people talk about various topics. Some topics are hot while others are unpopular. It’s rather a hard job for a person to find out hot topics in these tons of information. In this paper we introduce a system that automatically retrieves hot topics on BBS. Unlike some topic detection systems, this system not only discovers topics but also judges their hotness. Messages are first clustered into topics based on their lexical similarity. Then a BPNN (Back-Propagation Neural Network) based classification algorithm is used to judge the hotness of topic according to its popularity, its quality as well as its message distribution over time. We have conducted experiments over Yahoo! Message Board (Yahoo BBS) and retrieved satisfactory results.
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© 2005 Springer-Verlag Berlin Heidelberg
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You, L., Du, Y., Ge, J., Huang, X., Wu, L. (2005). BBS Based Hot Topic Retrieval Using Back-Propagation Neural Network. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_15
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DOI: https://doi.org/10.1007/978-3-540-30211-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24475-2
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