Optimizing Support Vector Machine (SVM) for Sentiment Analysis of Blu by BCA Reviews with Chi-Square
DOI:
https://doi.org/10.30871/jaic.v9i5.10541Keywords:
Sentiment Analysis, Support Vector Machine, Term Frequency-Inverse , Document Frequency, Chi-Square, SMOTEAbstract
One of the products resulting from the development of financial technology is the blu by BCA application. This app can be downloaded by BCA bank users via the Google Play Store and has received various user responses in the form of reviews. Analyzing these user reviews can serve as a valuable reference for further development and decision-making by BCA regarding the blu app. Sentiment analysis is conducted using the Support Vector Machine (SVM) algorithm, with SMOTE and TF-IDF techniques, and feature selection via Chi-Square. Sentiment classification using the SVM algorithm and feature selection has produced various outcomes in previous studies. Therefore, further research is necessary to analyze reviews of the blu application. This study aims to optimize the SVM method in analyzing user sentiment on the blu by BCA application by applying Chi-Square feature selection to improve sentiment classification performance. The research method includes the following stages: scraping, preprocessing, labeling, TF-IDF transformation, Chi-Square feature selection, SMOTE, data splitting, data mining, and evaluation. The testing results show that the RBF kernel achieved the highest performance with an accuracy of 0.8623, precision of 0.8623, recall of 0.8623, and F1-score of 0.8623. After applying Chi-Square feature selection, the accuracy improved to 0.8726, with precision of 0.8747, recall of 0.8725, and F1-score of 0.8723. This optimization successfully increased the accuracy by 0.0103 or 1.03%, while also improving precision, recall, and F1-score, indicating that feature selection contributes significantly to sentiment classification performance.
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