<![CDATA[The gubernatorial candidate debate was broadcast live streaming through various YouTube channels, which attracted public attention. Many discussions and conversations appeared in the comments section of each YouTube channel that broadcasted the debate. Given the numerous public discussions, it is undoubtedly interesting to analyze the contents of the conversations, as well as the expectations and feedback from the public. However, analyzing conversations in the form of text data will be challenging using conventional methods. Therefore, in this study, public opinion will be analyzed using the topic identification and sentiment classification approaches. Topic identification is conducted to obtain accurate information about what the public is discussing, while sentiment classification is used to determine whether each comment contains positive or negative sentiments. This research is novel because it utilizes data collected from various major media YouTube channels and includes a qualitative analysis of the findings. This study uses public comment data taken from the KPU, NarasiTV, and KompasTV YouTube channels; the results obtained included 4,147 data points. Data preprocessing involves identifying topics using the LDA method, evaluating the LDA model, performing sentiment classification using IndoBERT, and visualizing the results of the public opinion analysis. The results revealed five topics with a perplexity value of -7.7909 and a coherence score of 0.5109. In addition, topic 4 is the most dominant compared to other topics, with 1,146 comments classified as positive sentiment and 504 classified as negative sentiment. Topic 4 reflects how religion, culture, and frequently mentioned figures are perceived and discussed by the public, especially in relation to the gubernatorial election (pilgub) or gubernatorial candidate debates.]]>
