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With the rapid growth of Text sentiment analysis, the demand for automaticclassification of electronic documents has increased by leaps and bound. Theparadigm of text classification or text mining has been the subject of manyresearch works in recent time. In this paper we propose a technique for textsentiment classification using term frequency- inverse document frequency(TF-IDF) along with Next Word Negation (NWN). We have also compared theperformances of binary bag of words model, TF-IDF model and TF-IDF with nextword negation (TF-IDF-NWN) model for text classification. Our proposed model isthen applied on three different text mining algorithms and we found the LinearSupport vector machine (LSVM) is the most appropriate to work with our proposedmodel. The achieved results show significant increase in accuracy compared toearlier methods.

An Improved Text Sentiment Classification Model Using TF-IDF and Next Word Negation