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International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-9 Issue-44 September-2019
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Paper Title : A review of feature selection in sentiment analysis using information gain and domain specific ontology
Author Name : Ibrahim Said Ahmad, Azuraliza Abu Bakar and Mohd Ridzwan Yaakub
Abstract :

There is a continued interest in understanding people’s interest through the contents they share online. However, the data generated is massive, characterized by textual jargons and tokens that contain no sentiment or opinion value. One way of reducing the data dimension and pruning of irrelevant features is feature selection. However, the existing approaches of feature selection are still inefficient. Two prominent feature selection methods in sentiment analysis are information gain and ontology-based methods. Information gain has the disadvantage of not considering redundancy between features while ontology-based approach requires a lot of human intervention. The aim of this paper is to review these two methods. The review of these two methods shows that using the two methods in a two-step approach can overcome their limitations and provide an optimal feature set for sentiment analysis.
Keywords : Sentiment analysis, Feature selection, Information gain, Ontology.
Cite this article : Ahmad IS, Bakar AA, Yaakub MR. A review of feature selection in sentiment analysis using information gain and domain specific ontology. International Journal of Advanced Computer Research. 2019; 9(44):283-292. DOI:10.19101/IJACR.PID90.
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