Comparative analysis of effect of stopwords removal on sentiment classification

Abstract

Classification refers to the computational techniques for classifying whether the sentiments of text are positive or negative. Sentiment Classification being a specialized domain of text mining is expected to benefit after preprocessing such as removing stopwords. Stopwords are frequently occurring words that hardly carry any information and orientation. In this paper the effect of stopwords removal on various sentiment classification models was analyzed. Sentiment Classification models were evaluated using the movie document dataset. Accuracy increased from unprocessed dataset to stopwords removed dataset for Traditional Sentiment Classifiers. Our classifiers had hardly any impact of stopwords removal which indicates that they handled stopwords at the time of classification itself. Our classifiers also displayed accuracy better than traditional classifier and another surveyed classifier based on term weighting technique.