School of Computer & Systems Sciences

Ojha, A.C. and Shah, P.K. and Gupta, S. and Sharma, S. “Classifying Twitter Sentiment on Multi-Levels using A Hybrid Machine Learning Model”, “International Journal of Intelligent Systems and Applications in Engineering”, 2024,vol.12, pp. 328-333. The research paper proposes a hybrid machine learning model for multi-level sentiment classification of Twitter data. By combining the strengths of Spiking Neural Networks (SNN) and Naive Bayes (NB) classifiers, the proposed SNN+NB model effectively addresses the challenges of sentiment analysis on social media data. The model demonstrates improved accuracy, precision, recall, and F1-score compared to traditional methods. This approach offers valuable insights for understanding public opinion and making data-driven decisions.