<![CDATA[User reviews on digital platforms provide valuable insights into user experience; however, the large volume and unstructured nature of such data make systematic analysis challenging. In the case of the Vidio application, user feedback frequently reflects concerns related to advertisements, subscription systems, and technical performance. Nevertheless, existing researches often apply sentiment analysis and topic modeling separately, limiting the ability to understand how specific discussion themes are associated with user sentiment. To address this limitation, this research proposes an integrated topic–sentiment analysis approach for analyzing user reviews of the Vidio application collected from the Google Play Store. After filtering and quality control, 8,854 reviews were retained for further analysis using BERTopic for topic modeling and IndoRoBERTa for sentiment classification. The topic modeling process was optimized through parameter tuning, resulting in an improvement of the coherence score from 0.4076 to 0.6878, indicating better semantic consistency among the identified topics. Meanwhile, the sentiment classification model achieved an accuracy of 72%, although its performance was affected by class imbalance, particularly in identifying neutral sentiment. The analysis identified seven primary topics, where advertising-related issues emerged as the dominant topic and were strongly associated with negative sentiment, followed by concerns regarding subscription mechanisms and login accessibility. In contrast, content-related topics, particularly sports broadcasts, were consistently associated with positive sentiment. Furthermore, statistical evaluation confirmed a significant relationship between topic categories and sentiment distribution. Overall, the findings demonstrate that integrating topic modeling and sentiment analysis provides a more comprehensive understanding of user opinions and can support improvements in application quality and user experience.]]>
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