Polarity Detection of Top IPL Players' Tweets Using BERT and SVM based on tweets

Abstract

Sentiment analysis represents a fundamental component of natural language processing, playing a pivotal role in understanding user sentiments and public opinion across diverse domains. This research undertakes a comparative analysis of various machine learning methodologies for sentiment analysis, focusing specifically on polarity. The models evaluated include SVM with both Linear and Non-Linear (RBF) kernels, as well as BERT and ROBERTA. The primary metric employed to gauge the performance of each model is accuracy. The findings reveal that SVM with a Linear kernel achieved an accuracy of 84%, while the Non-Linear SVM (RBF) recorded an accuracy of 78%. BERT, when not fine-tuned, demonstrated an accuracy of 89%, which improved to 90% following fine-tuning. ROBERTA also achieved an accuracy of 90%. The insights derived from this study offer valuable guidance regarding the most effective machine learning models for sentiment analysis tasks. Researchers and practitioners can leverage these findings to select appropriate sentiment analysis models tailored to their specific application requirements. The research highlights the relative ease of enhancing accuracy through fine-tuning and the application of various pre-processing techniques. Furthermore, it underscores the importance of considering the wide range and scope of machine learning approaches to enhance and increase the accuracy and reliability of opinion mining and sentiment analysis systems in real-world applications.