Application of SMOTE in sentiment analysis of MyXL user reviews on Google Play Store

Badriyah, Badriyah, Chamidy, Totok and Suhartono, Suhartono (2025) Application of SMOTE in sentiment analysis of MyXL user reviews on Google Play Store. JISKA (Jurnal Informatika Sunan Kalijaga), 10 (1). pp. 74-86. ISSN 2528-0074

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Abstract

Texts that express customer opinions about a product are important input for companies. Companies obtain valuable information from consumer perceptions of marketed products by conducting sentiment analysis. However, real-world text datasets are often unbalanced, causing the prediction results of classification algorithms to be biased towards the majority class and ignore the minority class. This study analyzes the sentiment of MyXL user reviews on the Google Play Store by comparing the performance of the Logistic Regression and Support Vector Machine algorithms in the SMOTE implementation. This analysis uses TF-IDF to extract feature and GridSearchCV to optimize the accuracy, precision, recall, and F1 score evaluation metrics. This study follows several scenarios of dividing training data and test data. SVM implementing SMOTE is the algorithm with the best performance using the division of training data (90%) and test data (10%), resulting in accuracy (73.00%), precision (67.13%), recall (65.82%) and F1 score (66.30%).

Item Type:	Journal Article
Keywords:	Sentiment Analysis; Logistic Regression; Support Vector Machine; GridSearchCV; SMOTE
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Totok Chamidy
Date Deposited:	11 Jun 2025 07:59

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