Effect of Feature Reduction in Sentiment analysis of online reviews
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2013, Vol 2, Issue 6
Abstract
Sentiment analysis is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In sentiment analysis, feature reduction is a strategy that aims at making classifiers more efficient and accurate. Unfortunately, the huge number of features found in online reviews makes many of the potential applications of sentiment analysis infeasible. In this paper we evaluate the effect of a feature reduction method with both Support Vector Machine and Naive Bayes classifiers. The feature reduction method used is principle component analysis. Our results show that it is possible to maintain a state-of-the art classification accuracy while using less number of the features through Receiver operating characteristic curves and accuracy measures.
Authors and Affiliations
G. Vinodhini , RM. Chandrasekaran
Keywords
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