Abstract
This study explores sentiment analysis of Instagram app reviews using Long Short-Term Memory (LSTM) algorithms. The rise of app stores has transformed digital interactions, particularly for social media apps. Leveraging LSTM, we aim to understand user sentiments expressed in Instagram application reviews, offering insights to enhance user experience and address concerns. The methodology involves data crawling, preprocessing, LSTM model training, and evaluation metrics. Our findings reveal promising results in accurately identifying user sentiments, with an accuracy of 77.77%, precision of 0.45, recall of 0.089, and F1-score of 0.15. This study underscores the importance of sentiment analysis in understanding user feedback and its implications for app development and user engagement.
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