A Comparison Among Significance Tests and Other Feature Building Methods for Sentiment Analysis: A First Study

Words that participate in the sentiment (positive or negative) classification decision are known as significant words for sentiment classification. Identification of such significant words as features from the corpus reduces the amount of irrelevant information in the feature set under supervised sentiment classification settings. In this paper, we conceptually study and compare various types of feature building methods, viz., unigrams, TFIDF, Relief, Delta-TFIDF, \(\chi ^2\) test and Welch’s t-test for sentiment analysis task. Unigrams and TFIDF are the classic ways of feature building from the corpus. Relief, Delta-TFIDF and \(\chi ^2\) test have recently attracted much attention for their potential use as feature building methods in sentiment analysis. On the contrary, t-test is the least explored for the identification of significant words from the corpus as features.

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