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

TL;DR

This paper introduces an enhanced sentiment classification approach combining TF-IDF with Next Word Negation, demonstrating improved accuracy across multiple algorithms, especially with Linear SVM, for automatic electronic document analysis.

Contribution

The paper proposes integrating Next Word Negation with TF-IDF for sentiment classification, showing significant accuracy improvements over traditional models.

Findings

TF-IDF-NWN outperforms binary bag of words and TF-IDF alone.

Linear SVM achieves highest accuracy with the proposed model.

Significant accuracy increase compared to previous methods.

Abstract

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