Abstract
Internet is becoming a spreading platform for the public opinion. It is important to grasp the internet public opinion (IPO) in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in a number of information management and retrieval tasks. This paper studies internet public opinion hotspot detection using text mining approaches. First, we create an algorithm to obtain vector space model for all of text document. Second, this algorithm is combined with K-means clustering algorithm to develop unsupervised text mining approach. We use the proposed text mining approach to group the internet public opinion into various clusters, with the center of each representing a hotspot public opinion within the current time span. Through the result of the experiment, it shows that the efficiency and effectiveness of the algorithm using.
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Liu, H., Li, X. (2010). Internet Public Opinion Hotspot Detection Research Based on K-means Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_78
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DOI: https://doi.org/10.1007/978-3-642-13498-2_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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