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Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

R. Madhumathi1,*, A. Meena Kowshalya2, R. Shruthi1

1 Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, Coimbatore, India
2 Government College of Technology, Coimbatore, India

* Corresponding Author: R. Madhumathi. Email: email

Computer Systems Science and Engineering 2022, 43(2), 849-860. https://doi.org/10.32604/csse.2022.023568

Abstract

Sentiment analysis is the process of determining the intention or emotion behind an article. The subjective information from the context is analyzed by the sentimental analysis of the people’s opinion. The data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual polarity. In social behavior, sentiment can be thought of as a latent variable. Measuring and comprehending this behavior could help us to better understand the social issues. Because sentiments are domain specific, sentimental analysis in a specific context is critical in any real-world scenario. Textual sentiment analysis is done in sentence, document level and feature levels. This work introduces a new Information Gain based Feature Selection (IGbFS) algorithm for selecting highly correlated features eliminating irrelevant and redundant ones. Extensive textual sentiment analysis on sentence, document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection algorithm. The analysis is done based on the datasets from Cornell and Kaggle repositories. When compared to existing baseline classifiers, the suggested Information Gain based classifier resulted in an increased accuracy of 96% for document, 97.4% for sentence and 98.5% for feature levels respectively. Also, the proposed method is tested with IMDB, Yelp 2013 and Yelp 2014 datasets. Experimental results for these high dimensional datasets give increased accuracy of 95%, 96% and 98% for the proposed Information Gain based classifier for document, sentence and feature levels respectively compared to existing baseline classifiers.

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APA Style
Madhumathi, R., Kowshalya, A.M., Shruthi, R. (2022). Assessment of sentiment analysis using information gain based feature selection approach. Computer Systems Science and Engineering, 43(2), 849-860. https://doi.org/10.32604/csse.2022.023568
Vancouver Style
Madhumathi R, Kowshalya AM, Shruthi R. Assessment of sentiment analysis using information gain based feature selection approach. Comput Syst Sci Eng. 2022;43(2):849-860 https://doi.org/10.32604/csse.2022.023568
IEEE Style
R. Madhumathi, A.M. Kowshalya, and R. Shruthi "Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach," Comput. Syst. Sci. Eng., vol. 43, no. 2, pp. 849-860. 2022. https://doi.org/10.32604/csse.2022.023568



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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