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Students' feedback is crucial for academic institutions in order to evaluate faculty performance. The study involves the collection of diverse student feedback data and utilizes state-of-the-art natural language processing and sentiment analysis algorithms, including Keras, to automatically extract specific aspects and sentiments from the feedback. By categorizing sentiments as positive, negative, or neutral, the research unveils the key factors influencing student satisfaction within the educational realm. The culmination of this work results in the development of a predictive sentiment analysis model capable of automatically assessing overall feedback sentiment, thus empowering educational institutions with actionable insights to enhance program quality and create more student-centric and effective learning environments. [ABSTRACT FROM AUTHOR]