This paper introduces the clustering-based sentiment analysis approach which is a new approach to sentiment analysis. By applying a TF-IDF weighting method, voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. It has competitive advantages over the two existing kinds of approaches: symbolic techniques and supervised learning methods. It is a well performed, efficient, and non-human participating approach on solving sentiment analysis problems. © 2010 IEEE.
CITATION STYLE
Li, G., & Liu, F. (2010). A clustering-based approach on sentiment analysis. In Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010 (pp. 331–337). https://doi.org/10.1109/ISKE.2010.5680859
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