Sentiment analysis in social media has gained significant attention due to its ability to interpret public opinions and emotions. Natural Language Processing (NLP) plays an important role in automating this analysis, making it possible to process vast amounts of usergenerated content powerfully. However, numerous challenges exist, such as language ambiguity, sarcasm, and data quality issues, which make accurate sentiment classification difficult. This paper explores the various challenges associated with sentiment analysis in social media and highlights recent innovations that improve accuracy and efficiency. By leveraging advanced NLP techniques such as deep learning, transformer models, and hybrid approaches, sentiment analysis can be enhanced to better understand human emotions. This paper provides a comprehensive overview of sentiment analysis, its methodologies, applications, challenges, and future prospects, offering valuable insights into how sentiment analysis can be optimized for social media data.
Sentiment Analysis, Natural Language Processing (NLP), Social Media, Machine Learning, Deep Learning, Transformer Models