Leveraging Sentiment Analysis in the Digital Era: Uncovering Insights from Unstructured Data for Enhanced Customer Engagement

Abstract

The Google Play Store is a dynamic marketplace hosting a vast array of mobile applications across various categories. Analyzing user ratings and sentiments is essential for developers, marketers, and researchers to evaluate app performance and enhance user satisfaction. This study employs deep learning techniques, specifically a Long Short-Term Memory (LSTM)-based model, to examine user reviews and identify sentiment patterns. By leveraging natural language processing (NLP) and machine learning, the research investigates correlations between user feedback, app features, and overall ratings. The model processes and classifies user sentiments, such as positive, neutral, or negative and provides insights into key factors influencing user perceptions. Additionally, this study explores how app quality, functionality, and user engagement impact consumer satisfaction. Through data-driven analysis, it highlights the primary drivers of positive and negative reviews, offering a comprehensive understanding of user expectations and industry trends.