Abstract: Customer feedback has grown in importance as a source of information on user satisfaction, requirements, and expectations due to the rise of digital communication and the growth of online platforms. But sifting through thousands of assessments by hand is slow and frequently wrong, which emphasizes the need for automated alternatives. A refined DistilBERT transformer model that can automatically categorize app reviews into positive, negative, or neutral sentiments is shown in this study. Even in complex language that include slang, acronyms, sarcasm, or emoticons, the model is able to identify emotions and tone because to transformer-based contextual embeddings. The process begins with gathering and cleaning review data, removing superfluous symbols, tokenizing text, and then using the DistilBERT tokenizer to transform it into numerical form.The model is then fine-tuned on a labeled dataset to capture sentiment patterns and contextual relationships accurately. To assess its effectiveness, performance metrics such as accuracy, precision, recall, and F1-score are used, ensuring dependable results.In summary, this system provides an intelligent, efficient, and scalable approach for businesses to automatically analyze customer feedback, track sentiment trends, and make data-informed decisions that improve overall user experience and satisfaction.

Keywords App Store Reviews, Emotion analysis, Sentiment classification, Sentiment features, Machine learning, Natural Language Processing (NLP).

Downloads: | DOI: 10.17148/IJIREEICE.2025.131118