In the ever-expanding digital landscape, content creators rely heavily on user feedback to understand audience sentiments and preferences. This research proposes to enhance sentiment analysis by integrating multi-class classification, emoji detection, and a recommendation system. This involves the development of a comprehensive workflow for processing user comments. Initially, a diverse dataset is collected, encompassing various sentiments, topics, and emoji expressions. The data undergoes thorough preprocessing, including text cleaning, tokenization, and emoji transformation. For sentiment analysis, models employing Support Vector Machines (SVM) and Random Forest classifiers are trained on the preprocessed text data. To further enrich user experience, a recommendation system is implemented, offering personalized content suggestions based on user engagement and feedback. The recommendation system employs content-based filtering algorithms, ensuring content creators receive tailored insights into their audience’s preferences. This paper not only advances sentiment analysis capabilities but also empowers content creators with actionable insights for refining their content strategy. The combination of multi-class classification, emoji detection, and recommendation systems offers a holistic approach to understanding and engaging with user sentiments in the ever-changing field of producing digital content.