Sentiment Analysis of Social Media Data: Understanding Public Perception

Abstract

This paper provides a sentiment analysis model that combines dimensionality reduction, part-of-speech tagging, and natural language processing (NLP) for social media data. The model uses machine learning methods (Naive Bayes, Support Vector Machine, and K-Nearest Neighbor) to categorize sentiment as positive, negative, or neutral properly. The model's performance was assessed using two datasets and compared to other sentiment analysis algorithms that were already in use. The outcomes show increased performance and offer perceptions of the public's thoughts on various topics. This work addresses the problem of language-specific models and advances the creation of accurate sentiment analysis models. In contrast to conventional polls, the study's conclusions present a novel viewpoint on public opinion and offer suggestions for improving the platform so that users can access additional options and conveniences. The proposed model has potential applications in social media monitoring, market research, and political analysis. Future work can extend the model to accommodate multiple languages and explore the use of deep learning techniques. By providing a more accurate and efficient sentiment analysis tool, this research contributes to the growing field of social media analytic and its practical applications.