International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 11, Issue 8 (August 2024), Pages: 146-157

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 Original Research Paper

Sentiment analysis of movie review classifications using deep learning approaches

 Author(s): 

 Sarwar Shah Khan 1, 2, *, Yasser Alharbi 3

 Affiliation(s):

 1Department of Computer and Software Technology, University of Swat, Swat, Pakistan
 2Department of Computer Science, IQRA National University, Swat, Pakistan
 3College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia

 Full text

  Full Text - PDF

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-6387-4114

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2024.08.016

 Abstract

Movie reviews reflect how the public feels about a movie they have watched. However, because many reviews are posted on various websites, it is practically impossible to read each one. Summarizing all movie reviews can help people make informed decisions without reading through all of them. Previous studies have used different machine learning and deep learning techniques for sentiment analysis (SA), but few have combined comprehensive hyperparameter tuning and novel datasets for better performance. This paper presents an SA approach using deep learning models with optimized hyperparameters and a novel Rotten Tomatoes (RT) dataset to help viewers make better movie choices. SA, or opinion mining, is a computational technique to extract and analyze opinions and emotions expressed in text. We explore deep learning models such as Long Short-Term Memory (LSTM), XLNet, Convolutional Neural Networks-LSTM (CNN-LSTM), and Bidirectional Encoder Representations from Transformers (BERT). These models are known for capturing complex language patterns and context from raw text data. XLNet, a pre-trained model, effectively understands context by considering all possible permutations of the input sequence, BERT excels at using bidirectional context to understand text, LSTM retains information about long-term patterns in sequential data, and CNN-LSTM combines local and global context for reliable feature extraction. The RT dataset was pre-processed with data cleaning, spelling correction, lemmatization, and handling of informal words to improve the results. Our experiments show that XLNet performed better than other models on the Rotten Tomatoes dataset. The study demonstrates that SA of movie reviews provides insights into emotions and attitudes, allowing us to estimate a movie’s performance based on its overall sentiment.

 © 2024 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Sentiment analysis, Deep learning models, XLNet, Rotten Tomatoes dataset, Movie reviews

 Article history

 Received 7 April 2024, Received in revised form 9 August 2024, Accepted 18 August 2024

 Acknowledgment 

No Acknowledgment.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Khan SS and Alharbi Y (2024). Sentiment analysis of movie review classifications using deep learning approaches. International Journal of Advanced and Applied Sciences, 11(8): 146-157

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 Fig. 13 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 

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