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Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews

by Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 1
Year of Publication: 2016
Authors: Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah
10.5120/ijca2016909660

Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah . Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews. International Journal of Computer Applications. 142, 1 ( May 2016), 20-26. DOI=10.5120/ijca2016909660

@article{ 10.5120/ijca2016909660,
author = { Raj K. Palkar, Kewal D. Gala, Meet M. Shah, Jay N. Shah },
title = { Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 1 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number1/24861-2016909660/ },
doi = { 10.5120/ijca2016909660 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:46.629930+05:30
%A Raj K. Palkar
%A Kewal D. Gala
%A Meet M. Shah
%A Jay N. Shah
%T Comparative Evaluation of Supervised Learning Algorithms for Sentiment Analysis of Movie Reviews
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 1
%P 20-26
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Online forums and social networking websites provide users with a platform for expressing their opinions. Manually evaluating these reviews for crucial analytical information is cumbersome. Sentiment analysis deals with analyzing such massively available textual data and determining its polarity. This research paper provides a comparative study of multiple wellknown supervised machine learning algorithms on three standard datasets confined to the domain of movie reviews. The study is supported by illustrative plots and experimental results. The research work can be used as a base for further exploration in predicting the sentiment value of textual data in alternate domains using advanced machine learning algorithms.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Text classification Naïve Bayes Support Vector Machine Maximum Entropy Classification and Regression Trees Random Forest movie reviews.

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