<![CDATA[Social media platforms like Instagram play a crucial role in shaping public opinion and fostering community engagement in the digital age. The "StandWithUs" Instagram account, dedicated to raising awareness and advocacy, has garnered significant attention and interaction through its posts. However, understanding the sentiment behind user comments on these posts remains challenging. This study addresses this issue by employing Naive Bayes Classification to analyze the sentiment of comments on the "StandWithUs" Instagram account. The primary objective is to accurately classify comments into positive, negative, or neutral categories, providing insights into public opinion and engagement.Our findings indicate that the Naive Bayes Classification model achieves high accuracy in sentiment identification when trained with a substantial dataset. This research highlights the effectiveness of Naive Bayes in conducting sentiment analysis on social media, underscoring its potential to enhance our understanding and management of public opinion on advocacy-related content. The implications of this study are significant, offering valuable perspectives on how social media sentiment analysis can be leveraged to gauge public reactions and involvement, ultimately contributing to more informed advocacy strategies and community engagement efforts.By accurately interpreting the sentiments of user comments, stakeholders can better understand public perception and tailor their content and messaging strategies to foster more meaningful and positive interactions within their digital communities.]]>
