<![CDATA[Sexual harassment is a social issue widely discussed on the social media platform X. However, the high volume of unstructured comments makes it difficult to manually identify the main topics of discussion. This study aims to identify the main topics in comments related to sexual harassment on X using the Latent Dirichlet Allocation (LDA) method. The data used consist of comments on the topic of sexual harassment collected from X during the 2024–2026 period. The research stages include data collection, data preparation, dictionary and corpus construction, LDA modeling with hyperparameter tuning, evaluation using coherence score, and topic interpretation based on dominant keywords and representative data. The results show that the best LDA model consists of four topics with a coherence score of 0.517. These four topics are interpreted as Handling Cases of Sexual Harassment in Educational Environments, Victims’ Experiences and Psychological Impacts, Cases of Sexual Harassment in Higher Education, and Protection Related to Sexual Harassment. These findings indicate that the LDA method is capable of identifying the main topics in sexual harassment comments and helping to organize unstructured social media data into information that is easier to understand. The contribution of this study is the proposed Latent Dirichlet Allocation (LDA)-based topic modeling approach with hyperparameter tuning to identify and organize unstructured sexual harassment comments on the social media platform X into coherent and interpretable topic clusters. The resulting topic mapping provides valuable insights into the issues that receive the greatest public attention and can serve as a foundation for understanding public concerns. Furthermore, these findings have the potential to support the development of victim support services, including telemedicine-based systems.]]>
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