Feature Selection Techniques for Enhancing App User Review Analysis

The rapid growth of user-generated content, particularly app user reviews, presents a significant challenge in analyzing and extracting useful insights. The unstructured nature, inconsistent quality, and large volume of these reviews make it difficult to identify relevant information for app maintenance and updates. This study addresses this challenge by evaluating the impact of different feature selection techniques on the performance of machine learning models in multi-label classification tasks for app user review analysis. Our findings indicate that the subset attributes derived from a combination of Information Gain, GINI Index, and Correlation Matrix can improve the performance of machine learning models. Using a Support Vector Machine with proposed innovative score-based zero shot technique, promising results were achieved on average with 96.75% precision, 69.39% recall, and 80.81% F1-Score. Additionally, high-quality features, such as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">percentage Difference</i> can enhance performance in multi-label classification tasks, providing valuable insights for practitioners and researchers. The research implications and significance highlight the practical applications and strategic value of these findings, contributing to the advancement of knowledge and practice in the field of software engineering, particularly for multi-label classification tasks.