Web-page classification through summarization
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Relationship Algebra for Computing in Social Networks and Social Network Based Applications
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Noise reduction through summarization for Web-page classification
Information Processing and Management: an International Journal
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02
Research and Design of Internet Public Opinion Analysis System
SSME '09 Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering
Using text mining and sentiment analysis for online forums hotspot detection and forecast
Decision Support Systems
Information Processing and Management: an International Journal
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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.