Exploring the Effectiveness of Various Machine Learning Models in Analyzing Sentiment from Twitter Data

This research assesses sentiment analysis on Twitter posts about Twitter tweets, comparing ma- chine learning techniques. Four different models are used to classify and their efficacy is evaluated. Addressing balanced datasets, SVM excels in balanced sentiment scenarios. Using metrics like accuracy and recall, the study offersinsights for decision-making in marketing and social studies. Emphasizing machine learning effectiveness, the research suggests improvements for sentiment analysis in diverse domains, particularly in understanding positive and negative Twitter tweets.