An In-Depth Analysis of Sentiment Polarity Using Various Machine Learning Algorithms

The skill of identifying and understanding the sentiments, feelings, and views contained within textual material, whether it be a single sentence or a lengthy document, is known as sentiment analysis. Twitter, Facebook, WhatsApp, and others generate vast and diverse data in the digital age. These data streams provide insightful information about the audience's perceptions and reactions to a variety of occasions, products, or topics, covering the positive and negative ends of the spectrum. This study uses a supervised machine learning framework to examine 5,877 tweets on IPL 2023, a prominent sporting event. The main goal is to identify the predominant sentiment polarity in these Twitter postings. The authors use supervised machine learning to categorize tweets as positive, negative, or neutral regarding a significant sports event, unveiling nuanced user sentiments. They then conduct a comprehensive performance assessment, comparing various classifiers based on crucial metrics like accuracy, sensitivity, and precision to determine the most reliable sentiment analysis model.