With the growing popularity of podcasts, traditional analytics often neglect content-driven insights essential for understanding listener engagement and sentiment. This study explores sentiment analysis in conversational and informational podcasts, utilizing advanced NLP models such as ALBERT and RoBERTa. Sentiment is categorized as positive, negative, or neutral, with the MELD dataset representing conversational contexts and the Shakespeare podcast exemplifying informational content. Comparative analysis reveals that deep learning models outperform traditional approaches, such as Naive Bayes and Linear SVC, in capturing nuanced emotions. These findings pave the way for integrating multi-modal data, improving thematic analysis, and advancing comprehensive podcast analytics platforms.