Open Access

ARTICLE

Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

by R. Madhumathi^1,*, 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:

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

Received 13 September 2021; Accepted 14 October 2021; Issue published 20 April 2022

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.

---

Keywords

---