Sentiment analysis is critical for extracting views and emotions from textual data, with applications including consumer feedback and social media insights. This paper shows a mixed system that combines Neuro-Symbolic Sentiment Analysis (using deep learning models to combine symbolic lexicon features) and Topic-Driven Sentiment Analysis (using techniques for latent variables). By combining symbolic thinking and current machine learning, we improve both interpretability and classification accuracy. The framework uses a number of methods for binary and multiclass sentiment categorization, including Logistic Regression, Naive Bayes, SVM, Random Forests, and Fully Connected Neural Networks (FCNN). To ensure reliability, we use k-fold cross-validation for model evaluation. The use of latent variable modeling reveals underlying thematic implications on sentiment classification. Experimental validation on a real-world sentiment dataset reveals the usefulness of our strategy, which achieves high accuracy while remaining comprehensible. This study demonstrates the utility of neurosymbolic and topic-driven modeling in enhancing understandable sentiment analysis.