<![CDATA[The rapid growth of mobile apps has overwhelmed developers with a large number of unorganized user reviews, which are difficult to analyze efficiently because traditional methods like Latent Dirichlet Allocation (LDA) often fail to capture the deeper meaning behind user language. To address this issue, our study introduces a novel hybrid topic modeling approach specifically designed to decipher user feedback for the Pluang app by combining state-of-the-art transformer-based AI with graph modeling. In our framework, we use Sentence-BERT (SBERT) to capture the true meaning of text, construct a network graph to map how different comments are connected, and apply Louvain community detection to cluster similar discussions, while evaluating performance using standard metrics such as topic coherence, silhouette score, and topic diversity. After collecting 2,345 reviews from the Google Play Store and refining them into a clean sample of 2,295, our experiments revealed that while the traditional LDA model had a higher coherence score (0.5367), our hybrid model successfully uncovered 15 specific and highly detailed topics that LDA completely missed even with a slightly lower coherence score of 0.4342. Ultimately, by combining semantic embedding with graph clustering, this hybrid approach provides a much clearer and more nuanced understanding of users' true concerns, helping developers make better, data-driven decisions for future mobile app updates.]]>
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