Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method

Research on films is fascinating because of the profound changes that the development of information and communication technology has brought about in our interactions with and consumption of media content. This study performs sentiment analysis on "Dune: Part Two" movie reviews using the Naïve Bayes method. Review data was collected from IMDb and then processed through several stages such as preprocessing, feature selection with TF-IDF, data splitting, and data mining and evaluation. Naïve Bayes was chosen for its simplicity and ability to handle large datasets effectively. The test results showed a high accuracy rate of 95%, indicating that this model can identify positive, negative, and neutral sentiments well. The use of TF-IDF in feature selection allowed the model to focus on important words, enhancing its sentiment classification ability. This research can provide insights into audience perceptions of the film "Dune: Part Two," which is beneficial for the film industry.

Authors

Publication Details

Journal
Journal Of Computer Networks Architecture and High Performance Computing
Published
2024-10-11
DOI
https://doi.org/10.47709/cnahpc.v6i4.4604
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
0.00
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article

Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method

Ahmad Turmudi Zy, Irfan Afriantoro, D. Maheswari
Journal Of Computer Networks Architecture and High Performance Computing
Fire effects on ecosystems
article

Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method

Ahmad Turmudi Zy, Irfan Afriantoro, D. Maheswari
article en

Abstract

Research on films is fascinating because of the profound changes that the development of information and communication technology has brought about in our interactions with and consumption of media content. This study performs sentiment analysis on "Dune: Part Two" movie reviews using the Naïve Bayes method. Review data was collected from IMDb and then processed through several stages such as preprocessing, feature selection with TF-IDF, data splitting, and data mining and evaluation. Naïve Bayes was chosen for its simplicity and ability to handle large datasets effectively. The test results showed a high accuracy rate of 95%, indicating that this model can identify positive, negative, and neutral sentiments well. The use of TF-IDF in feature selection allowed the model to focus on important words, enhancing its sentiment classification ability. This research can provide insights into audience perceptions of the film "Dune: Part Two," which is beneficial for the film industry.

Journal Of Computer Networks Architecture and High Performance ComputingVol. 6(4)
Openalex Percentile: Top 83%
Fire effects on ecosystems
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