The rapid growth of the skincare market, particularly in Indonesia, has made customer reviews a crucial source of insights for understanding consumer preferences and improving product offerings. However, the unstructured and voluminous nature of online reviews presents challenges in extracting actionable information. This study aims to explore key product features in sunscreen reviews by integrating Topic Modeling, Sentiment Analy- sis, and Social Network Analysis (SNA). Sentence Segment-LDA (SS-LDA) was applied to extract dominant product features, uncovering seven main themes, including texture and consistency, irritation and side effects, UV protection, skin compatibility, usage context and ingredients, ease of use, and affordability. Aspect-Based Sentiment Analysis (ABSA) was conducted using a hybrid approach combining TF-IDF and polarity scores, achieving an overall accuracy of 66%, with strong performance in identifying positive sentiments. Social Network Analysis revealed that ”price” emerged as the most central and influential terms in customer discussions. The integrated analytical framework provides a compre- hensive understanding of customer concerns and priorities, offering valuable insights to support product development, marketing strategies, and customer satisfaction initiatives in the skincare industry.