Sentiment Analysis of the Film "JUMBO" on Twitter Using the Naive Bayes Method and Support Vector Machine (SVM) with a Text Mining Approach
This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through sev...
Saved in:
| Published in: | Journal of Applied Informatics and Computing Vol. 9; no. 5; pp. 2861 - 2868 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English Indonesian |
| Published: |
Politeknik Negeri Batam
19.10.2025
|
| Subjects: | |
| ISSN: | 2548-6861, 2548-6861 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions.
This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions. |
|---|---|
| AbstractList | This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions. This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions. This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector Machine (SVM) methods. The data were gathered through a crawling process on Twitter, yielding 2,011 tweets, which were then processed through several pre-processing steps, including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Subsequently, the data were transformed into numerical representations using TF-IDF, followed by sentiment labeling into positive, negative, and neutral categories. For the Naive Bayes method, training and evaluation were conducted using 5-fold Cross Validation. The results showed that the Naive Bayes model achieved an accuracy of 80.60%, precision of 73.83%, recall of 73.50%, and an F1-score of 69.98%. Meanwhile, the SVM method obtained an accuracy of 75.87%, precision of 76.36%, recall of 62.45%, and an F1-score of 65.64%. Compared to the baseline random classifier, which only achieved an accuracy of 32.47%, both primary methods significantly outperformed it in classifying film review sentiments. The analysis also indicates that the F1-score is lower than the accuracy due to the imbalanced data distribution, with a considerably higher number of positive reviews. This study also presents visualizations of sentiment distribution and word clouds to provide a clearer understanding of audience opinions. The results demonstrate that the Naive Bayes method performs well and has potential for use in sentiment analysis of films on social media platforms. These findings are expected to provide valuable insights for the creative industry, particularly in evaluating audience responses and improving the quality of future film productions. |
| Author | Fajri, Ika Nur Widodo, Tegar Robi Sari, Bety Wulan |
| Author_xml | – sequence: 1 givenname: Tegar Robi surname: Widodo fullname: Widodo, Tegar Robi – sequence: 2 givenname: Ika Nur surname: Fajri fullname: Fajri, Ika Nur – sequence: 3 givenname: Bety Wulan surname: Sari fullname: Sari, Bety Wulan |
| BookMark | eNpNkU1PGzEQhq0KpFLgznHEqT0k9ceu4z0GVFoqFg5JuFqOd0wcJfbKdoH8hv7pbkKFuMyMZl49h3m-kKMQAxJywehYUDVh39fG2_Fz4-sxo3U9-UROeF2pkVSSHX2YP5PznNeUUt4wLjk7IX9nGIrfDgWmwWx22WeIDsoK4cZvtnD5e9FePVxCDDB_8aVggkX24emQuDf-GeHK7DBDi2UVOzChg9mfvo-pwCPaEhO0xq58QPg6e2y_wQBZgYE5vhZofdijpn2f4hA6I8fObDKe_--nZHHzY379a3T38PP2eno3sqxqJqOuM9QJ0dkllWg7U0uFkluzdNIq7hpXywYrUSnHTVNLVw13yqxEKqhTbiJOye0bt4tmrfvktybtdDReHxYxPWmTircb1FVHkavhf1bKSjRcCSEVXYqKKqdqdAOLvrFsijkndO88RvXBjd670Xs3-uBG_APH8oQN |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.30871/jaic.v9i5.10557 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2548-6861 |
| EndPage | 2868 |
| ExternalDocumentID | oai_doaj_org_article_4d0e28254c664392833680b3408f85ef 10_30871_jaic_v9i5_10557 |
| GroupedDBID | AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ |
| ID | FETCH-LOGICAL-c1497-dda0f33dcb06ecda568e62cabf6c82f9f569e4348f2a956f468e01c6e030f8f73 |
| IEDL.DBID | DOA |
| ISSN | 2548-6861 |
| IngestDate | Mon Dec 01 19:30:41 EST 2025 Thu Nov 27 00:42:02 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Issue | 5 |
| Language | English Indonesian |
| License | http://creativecommons.org/licenses/by-sa/4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1497-dda0f33dcb06ecda568e62cabf6c82f9f569e4348f2a956f468e01c6e030f8f73 |
| OpenAccessLink | https://doaj.org/article/4d0e28254c664392833680b3408f85ef |
| PageCount | 8 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_4d0e28254c664392833680b3408f85ef crossref_primary_10_30871_jaic_v9i5_10557 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-10-19 |
| PublicationDateYYYYMMDD | 2025-10-19 |
| PublicationDate_xml | – month: 10 year: 2025 text: 2025-10-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of Applied Informatics and Computing |
| PublicationYear | 2025 |
| Publisher | Politeknik Negeri Batam |
| Publisher_xml | – name: Politeknik Negeri Batam |
| SSID | ssj0002912621 |
| Score | 1.9251112 |
| Snippet | This study aims to perform sentiment analysis on reviews of the film “JUMBO” collected from the Twitter platform, using the Naive Bayes and Support Vector... |
| SourceID | doaj crossref |
| SourceType | Open Website Index Database |
| StartPage | 2861 |
| SubjectTerms | jumbo film naïve bayes random clasifier sentiment analysis support vector machine (svm) text mining twitter (x) |
| Title | Sentiment Analysis of the Film "JUMBO" on Twitter Using the Naive Bayes Method and Support Vector Machine (SVM) with a Text Mining Approach |
| URI | https://doaj.org/article/4d0e28254c664392833680b3408f85ef |
| Volume | 9 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2548-6861 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002912621 issn: 2548-6861 databaseCode: DOA dateStart: 20170101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3LSsQwFA0yuHAjiopvLuJCF6OZtEmT5Yw4iNBRmFHclTyhoB3xid_gT5ubzsjs3LhtQynntveVm3MIOS685MYyJLrlsUAxnncV7hRSkzsf4wFTPCSxiWI0kg8P6nZB6gtnwlp64Ba489xRj-crcysweMZomAlJTZZTGST3Ab0vLdRCMYU-mKkeE6zX7ksi6R3yDNX27EPVHKVtMRotxKEFuv4UV4ZrZHWWEEK_fZF1suSbDfI9xiEebNzBnDUEpgFisgbD-vEJjq7vysHNEUwbmHzWeCAH0t5_WjHS0YPBQH_5VyiTQDToxgHqd8ZcG-5Tnx7KNEXp4WR8X54CtmNBwyS6aiiTZgT0Z2zjm-RueDm5uOrOZBO6NpY7Rdc5TUOWOWuo8NZpLqQXzGoThJUsqMCF8nmWy8B0rI5CHu_TnhU-_u9BhiLbIp1m2vhtAjST1KFaYhAFSpVpyw1zJprQGGZ5tkNO5yBWzy07RhWrigR4hYBXCHiVAN8hA0T5dx3yWqcL0drVzNrVX9be_Y-H7JEVhiq-OJei9knn7eXdH5Bl-_FWv74cpg_pBxc0yrw |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Sentiment+Analysis+of+the+Film+%22JUMBO%22+on+Twitter+Using+the+Naive+Bayes+Method+and+Support+Vector+Machine+%28SVM%29+with+a+Text+Mining+Approach&rft.jtitle=Journal+of+Applied+Informatics+and+Computing&rft.au=Widodo%2C+Tegar+Robi&rft.au=Fajri%2C+Ika+Nur&rft.au=Sari%2C+Bety+Wulan&rft.date=2025-10-19&rft.issn=2548-6861&rft.eissn=2548-6861&rft.volume=9&rft.issue=5&rft.spage=2861&rft.epage=2868&rft_id=info:doi/10.30871%2Fjaic.v9i5.10557&rft.externalDBID=n%2Fa&rft.externalDocID=10_30871_jaic_v9i5_10557 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2548-6861&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2548-6861&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2548-6861&client=summon |