Movie Recommendation System Based on Sentiment Analysis on Movie Reviews
A movie review plays a significant role in determining whether the movie is recommended to them. Nowadays, movie reviews are filled with paid, sarcasm, and fake reviews that give users mixed feelings about the movie. With a movie recommendation system based on sentiment, the movie review is analyzed...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
INTI International University
2022
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1633/ http://eprints.intimal.edu.my/1633/1/jods2022_05.pdf |
| _version_ | 1848766791761788928 |
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| author | Astried, . Tri Basuki, Kurniawan Mohd Zaki, Zakaria Misinem, . Mohamed Fakhrul Faris, Mohamed Fuad |
| author_facet | Astried, . Tri Basuki, Kurniawan Mohd Zaki, Zakaria Misinem, . Mohamed Fakhrul Faris, Mohamed Fuad |
| author_sort | Astried, . |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | A movie review plays a significant role in determining whether the movie is recommended to them. Nowadays, movie reviews are filled with paid, sarcasm, and fake reviews that give users mixed feelings about the movie. With a movie recommendation system based on sentiment, the movie review is analyzed with specific keywords that give the user an absolute result on whether it is recommended or not recommended. The system uses three classifiers, Naïve Bayes, Support Vector Machines (SVM), and Deep Learning to determine the best classifier that gives better accuracy for user purposes. The core approach of this project is to provide the user with a simplified view that analyses all accumulated reviews into one single view. The project results show that SVM produces the best results with 81.17% accuracy. That result is because of the nature of the classification that works best in categorization. This project includes future works, adding more lists of movies and user input for better interactivity between users and machines. |
| first_indexed | 2025-11-14T11:56:46Z |
| format | Article |
| id | intimal-1633 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:56:46Z |
| publishDate | 2022 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-16332024-05-07T09:38:57Z http://eprints.intimal.edu.my/1633/ Movie Recommendation System Based on Sentiment Analysis on Movie Reviews Astried, . Tri Basuki, Kurniawan Mohd Zaki, Zakaria Misinem, . Mohamed Fakhrul Faris, Mohamed Fuad QA75 Electronic computers. Computer science QA76 Computer software A movie review plays a significant role in determining whether the movie is recommended to them. Nowadays, movie reviews are filled with paid, sarcasm, and fake reviews that give users mixed feelings about the movie. With a movie recommendation system based on sentiment, the movie review is analyzed with specific keywords that give the user an absolute result on whether it is recommended or not recommended. The system uses three classifiers, Naïve Bayes, Support Vector Machines (SVM), and Deep Learning to determine the best classifier that gives better accuracy for user purposes. The core approach of this project is to provide the user with a simplified view that analyses all accumulated reviews into one single view. The project results show that SVM produces the best results with 81.17% accuracy. That result is because of the nature of the classification that works best in categorization. This project includes future works, adding more lists of movies and user input for better interactivity between users and machines. INTI International University 2022-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1633/1/jods2022_05.pdf Astried, . and Tri Basuki, Kurniawan and Mohd Zaki, Zakaria and Misinem, . and Mohamed Fakhrul Faris, Mohamed Fuad (2022) Movie Recommendation System Based on Sentiment Analysis on Movie Reviews. Journal of Data Science, 2022 (05). pp. 1-12. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Astried, . Tri Basuki, Kurniawan Mohd Zaki, Zakaria Misinem, . Mohamed Fakhrul Faris, Mohamed Fuad Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title | Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title_full | Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title_fullStr | Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title_full_unstemmed | Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title_short | Movie Recommendation System Based on Sentiment Analysis on Movie Reviews |
| title_sort | movie recommendation system based on sentiment analysis on movie reviews |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1633/ http://eprints.intimal.edu.my/1633/ http://eprints.intimal.edu.my/1633/1/jods2022_05.pdf |