Analyzing Sentiments in Motorku X App Reviews using the Naive Bayes Classifier Approach

In the digital age, mobile applications have become ubiquitous, necessitating sophisticated mechanisms for gauging user sentiment to refine app development and enhance user engagement. This paper embarks on a novel exploration of sentiment analysis within the realm of Motorku X app reviews, employing the Naive Bayes Classifier to meticulously dissect user feedback. Recognized for its computational efficiency and accuracy in text classification, the Naive Bayes Classifier emerges as a pivotal tool in our analysis, enabling the categorization of sentiments into positive, neutral, and negative domains. The study meticulously compiles and preprocesses a substantial dataset of user reviews, applying the Naive Bayes algorithm to unveil the underlying sentiment spectrum. Our findings not only offer a nuanced understanding of user satisfaction but also highlight areas requiring attention, thus providing invaluable insights for app developers aiming to optimize user experience. Furthermore, this research contributes to the broader field of Natural Language Processing (NLP) by showcasing the applicability of machine learning techniques in real-world sentiment analysis tasks. By bridging the gap between user feedback and actionable insights, this study underscores the critical role of sentiment analysis in the iterative process of app development, offering a roadmap for leveraging user reviews as a catalyst for improvement.