Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Development and use of a gold-standard data set for subjectivity classifications
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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The purpose of web text mining is to find the potential knowledge from the immensity text information on the internet. In this paper, a novel web text mining method is proposed based on semantic polarity analysis. Firstly, the model for web text mining is presented by using semantic polarity analysis, which includes three main parts: data acquisition, feature sentences analysis and semantic polarity analysis. Secondly, the procedure with semantic polarity analysis is introduced for web text mining, and the related algorithms are also discussed. Thirdly, the method is applied into an actual case to try to find out the valuable products information for the consumers. The results show that the method is both reasonable and effective.