Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis
The rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LPPM ISB Atma Luhur
2025-07-01
|
| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2426 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849728897370816512 |
|---|---|
| author | Fenilinas Adi Artanto |
| author_facet | Fenilinas Adi Artanto |
| author_sort | Fenilinas Adi Artanto |
| collection | DOAJ |
| description | The rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a lack of studies focusing on cultural sentiment such as humor in the Italian brainroot anomaly. This study provides an overview of user sentiment analysis of the game “Hantu Tung Tung Tung Sahur 3D,” a culturally viral application anomaly italian brainroot among young people on the Google Play Store during the month of Ramadan. User reviews were collected through web scraping, and data preprocessing involved tokenization, stopword removal, lowercase, stemming, and filtering to prepare the text for analysis. Feature extraction was performed using the Bag of Words method. This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. The novelty of this study lies in its focus on sentiment analysis in a unique and culturally viral digital context, as well as a comparative evaluation of classification algorithms specifically on this dataset. The results show that the Random Forest algorithm achieves the highest Area Under the Curve (AUC) score of 0.529, outperforming Naïve Bayes (0.504), SVM (0.503), and Decision Tree (0.498). These findings provide new insights into the suitability of ensemble methods such as Random Forest for sentiment analysis in specific digital phenomena, highlighting its potential for more reliable sentiment classification in similar contexts. |
| format | Article |
| id | doaj-art-1384fbc751344b048004d1e94fcc61b5 |
| institution | DOAJ |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-1384fbc751344b048004d1e94fcc61b52025-08-20T03:09:24ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-07-0114342242810.32736/sisfokom.v14i3.24262089Comparison of Classification Algorithms with Bag of Words Feature in Sentiment AnalysisFenilinas Adi Artanto0Department of Informatics, Faculty of Engineering and Computer Science, Universitas Muhammadiyah Pekajangan PekalonganThe rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a lack of studies focusing on cultural sentiment such as humor in the Italian brainroot anomaly. This study provides an overview of user sentiment analysis of the game “Hantu Tung Tung Tung Sahur 3D,” a culturally viral application anomaly italian brainroot among young people on the Google Play Store during the month of Ramadan. User reviews were collected through web scraping, and data preprocessing involved tokenization, stopword removal, lowercase, stemming, and filtering to prepare the text for analysis. Feature extraction was performed using the Bag of Words method. This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. The novelty of this study lies in its focus on sentiment analysis in a unique and culturally viral digital context, as well as a comparative evaluation of classification algorithms specifically on this dataset. The results show that the Random Forest algorithm achieves the highest Area Under the Curve (AUC) score of 0.529, outperforming Naïve Bayes (0.504), SVM (0.503), and Decision Tree (0.498). These findings provide new insights into the suitability of ensemble methods such as Random Forest for sentiment analysis in specific digital phenomena, highlighting its potential for more reliable sentiment classification in similar contexts.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2426analysis sentimentanomaly trenbag of wordsgoogle play store |
| spellingShingle | Fenilinas Adi Artanto Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis Jurnal Sisfokom analysis sentiment anomaly tren bag of words google play store |
| title | Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis |
| title_full | Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis |
| title_fullStr | Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis |
| title_full_unstemmed | Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis |
| title_short | Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis |
| title_sort | comparison of classification algorithms with bag of words feature in sentiment analysis |
| topic | analysis sentiment anomaly tren bag of words google play store |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2426 |
| work_keys_str_mv | AT fenilinasadiartanto comparisonofclassificationalgorithmswithbagofwordsfeatureinsentimentanalysis |