<![CDATA[This study investigates the effectiveness of deep learning methods in analyzing linguistically diverse customer reviews on Shopee to generate actionable product insights. By integrating Word2Vec, Convolutional Neural Networks (CNN), and Deep K-Means clustering, the proposed workflow moves beyond simple polarity detection toward aspect-based sentiment analysis. Customer reviews were preprocessed and represented using Word2Vec (skip-gram) to capture semantic proximity across informal registers, slang, abbreviations, and code-switching. A one-dimensional CNN then classified reviews into positive and negative sentiments, achieving 93–94% accuracy with balanced F1-scores across both classes. To extract aspect-level insights, reviews were projected into a latent space via an autoencoder and clustered using K-Means, with evaluation metrics (Silhouette ≈ 0.6; DBI ≈ 0.5) confirming adequate cohesion and separation. Positive clusters highlighted product design, durability, and ease of use, while negative clusters emphasized material quality, packaging, and delivery issues. These findings demonstrate that deep learning can adapt to sociolinguistic variation in Indonesian e-commerce discourse while providing structured, socially meaningful insights. This research is significant for the field of Informatics as it advances Natural Language Processing techniques for multilingual and code-switched data, addressing a key challenge in real-world text mining applications. The approach offers practical value for sellers in improving product quality, enhancing customer satisfaction, and refining marketing strategies.]]>
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