Sentiment Analysis of Online Movie Reviews using Machine Learning

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.

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