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Assessment of Sentiment Analysis Using Information Gain Based Feature Selection Approach

Computer Systems Science & Engineering 2022年第11期43卷 849-860页

作者： R.Madhumathi A.Meena Kowshalya R.Shruthi Department of Computer Science and Engineering Sri Ramakrishna Engineering CollegeCoimbatoreIndia Government College of Technology CoimbatoreIndia

Sentiment analysis is the process of determining the intention or emotion behind an *** subjective information from the context is analyzed by the sentimental analysis of the people’s *** data that is analyzed quantifies the reactions or sentiments and reveals the information’s contextual *** social behavior,sentiment can be thought of as a latent *** and comprehending this behavior could help us to better understand the social *** sentiments are domain specific,sentimental analysis in a specific context is critical in any real-world *** sentiment analysis is done in sentence,document level and feature *** work introduces a new Information Gain based Feature Selection(IGbFS)algorithm for selecting highly correlated features eliminating irrelevant and redundant *** textual sentiment analysis on sentence,document and feature levels are performed by exploiting the proposed Information Gain based Feature Selection *** analysis is done based on the datasets from Cornell and Kaggle *** 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 ***,the proposed method is tested with IMDB,Yelp 2013 and Yelp 2014 *** 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.

关键词： Sentiment analysis sentence level document level feature level information gain

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