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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Sentiment Analysis of Online Movie Reviews using Machine Learning

Author 1: Isaiah Steinke
Author 2: Justin Wier
Author 3: Lindsay Simon
Author 4: Raed Seetan

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130973

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: Many websites encourage their users to write reviews for a wide variety of products and services. In particular, movie reviews may influence the decisions of potential viewers. However, users face the arduous tasks of summarizing the information in multiple reviews and determining the useful and relevant reviews among a very large number of reviews. Therefore, we developed machine learning (ML) models to classify whether an online movie review has positive or negative sentiment. We utilized the Stanford Large Movie Review Dataset to build models using decision trees, random forests, and support vector machines (SVMs). Further, we compiled a new dataset comprising reviews from IMDb posted in 2019 and 2020 to assess whether sentiment changed owing to the coronavirus disease 2019 (COVID-19) pandemic. Our results show that the random forests and SVM models provide the best classification accuracies of 85.27% and 86.18%, respectively. Further, we find that movie reviews became more negative in 2020. However, statistical tests show that this change in sentiment cannot be discerned from our model predictions.

Keywords: Decision tree; machine learning (ML); natural language processing (NLP); random forests; sentiment analysis; support vector machine (SVM); reviews

Isaiah Steinke, Justin Wier, Lindsay Simon and Raed Seetan, “Sentiment Analysis of Online Movie Reviews using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130973

@article{Steinke2022,
title = {Sentiment Analysis of Online Movie Reviews using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130973},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130973},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Isaiah Steinke and Justin Wier and Lindsay Simon and Raed Seetan}
}


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