A comparative study of TF*IDF, LSI and multi-words for text classification

Wen Zhang, Taketoshi Yoshida, Xijin Tang
2011 Expert systems with applications  
One of the main themes in text mining is text representation, which is fundamental and indispensable for text-based intellegent information processing. Generally, text representation inludes two tasks: indexing and weighting. This paper has comparatively studied TFÃIDF, LSI and multi-word for text representation. We used a Chinese and an English document collection to respectively evaluate the three methods in information retreival and text categorization. Experimental results have demonstrated
more » ... that in text categorization, LSI has better performance than other methods in both document collections. Also, LSI has produced the best performance in retrieving English documents. This outcome has shown that LSI has both favorable semantic and statistical quality and is different with the claim that LSI can not produce discriminative power for indexing.
doi:10.1016/j.eswa.2010.08.066 fatcat:oqggdpgkh5h7hbjl6cumcszyg4