<![CDATA[Online gambling has become a serious social issue due to its easy accessibility through digital platforms, requiring effective policy interventions. This study analyzes public sentiment toward online gambling by examining 10,000 YouTube comments using a Convolutional Neural Network (CNN) algorithm. Data were collected via the YouTube API and underwent preprocessing steps including text cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using a lexicon-based approach, with data transformed through Word2Vec embedding and balanced using oversampling techniques. The CNN model, consisting of embedding, convolutional, pooling, and dense layers, achieved an impressive accuracy of 99.10%, outperforming traditional machine learning methods. Sentiment was categorized into positive, neutral, and negative, with the majority of comments reflecting positive sentiment, indicating public support for efforts to combat online gambling. WordCloud visualizations highlighted dominant themes and frequently used terms. This study demonstrates the effectiveness of CNN in analyzing unstructured social media data and offers valuable insights for policymakers. Future research should explore hybrid architectures such as CNN-LSTM and expand datasets by including other platforms like Twitter, Instagram, and TikTok to enhance generalization and address broader social challenges.]]>
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