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A Machine Learning-Based Lexicon Approach for Sentiment Analysis

A Machine Learning-Based Lexicon Approach for Sentiment Analysis

Tirath Prasad Sahu, Sarang Khandekar
Copyright: © 2020 |Volume: 16 |Issue: 2 |Pages: 15
ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781799802778|DOI: 10.4018/IJTHI.2020040102
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MLA

Sahu, Tirath Prasad, and Sarang Khandekar. "A Machine Learning-Based Lexicon Approach for Sentiment Analysis." IJTHI vol.16, no.2 2020: pp.8-22. http://doi.org/10.4018/IJTHI.2020040102

APA

Sahu, T. P. & Khandekar, S. (2020). A Machine Learning-Based Lexicon Approach for Sentiment Analysis. International Journal of Technology and Human Interaction (IJTHI), 16(2), 8-22. http://doi.org/10.4018/IJTHI.2020040102

Chicago

Sahu, Tirath Prasad, and Sarang Khandekar. "A Machine Learning-Based Lexicon Approach for Sentiment Analysis," International Journal of Technology and Human Interaction (IJTHI) 16, no.2: 8-22. http://doi.org/10.4018/IJTHI.2020040102

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Abstract

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

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