Comparison of Chi-Square and Information Gain Feature Selection Methods for Support Vector Machine-Based Sentiment Analysis

Abstrak

Vidio is a local streaming platform that dominates the Indonesian market, but still faces challenges in improving user satisfaction as reflected by its 3.5 rating. To enhance the application, user experience insights are needed, which can be identified through sentiment analysis. This study aims to analyze the sentiment of Vidio application user reviews and compare the performance of the Support Vector Machine model using Chi-Square and Information Gain feature selection. The dataset comprises 4,670 reviews collected from July 01 to November 30, 2024. Model evaluation utilizes Balanced Accuracy metrics optimized through hyperparameter tuning to ensure fair assessment on imbalanced data. The experimental results demonstrate that Chi-Square feature selection yields the optimal performance, achieving a peak Balanced Accuracy of 94.78%. Significantly, this result was attained using a computationally efficient Linear Kernel (). In contrast, the Information Gain method yielded a lower Balanced Accuracy of 94.20% despite utilizing a complex Polynomial Kernel (). These findings conclude that Chi-Square provides a superior trade-off between classification accuracy and model complexity, offering a more robust solution for sentiment analysis.

Identifier

• DOI: https://doi.org/10.37278/sisinfo.v8i1.1352
• OpenAlex: https://openalex.org/W7133186797