Sentiment Analysis Using Machine Learning Methods on Social Media

Abstract

Sentiment analysis deals with understanding human feelings and opinions by analyzing the emotional content of words. With the rise of social media platforms, an immense volume of text data has become available for analysis. Machine learning (ML) techniques are essential to process this data and can provide businesses with deep insights into customer feedback, brand reputation, and emerging market trends. They also help governments and public organizations gauge public opinion on current events, proposed laws, and social issues. This study aims to improve the accuracy and effectiveness of sentiment analysis on social media (specifically Twitter) by applying advanced ML methods to address challenges of contextual understanding, noisy text preprocessing, and the evolving slang and vernacular of online content. We developed a sentiment classification model for Twitter data and evaluated several algorithms on a real-world dataset. The results show that a ML approach can successfully classify social media posts by sentiment, highlighting prevailing public moods in real time. In our experiments, an ensemble model outperformed other classifiers in balancing precision and recall, achieving high overall accuracy. These findings showcase the potential of ML methods in capturing the sentiment of social media discourse.