Authors: Dr. Meena Desai, Parched Saxena
Abstract: The rise of social media platforms has drastically transformed the way individuals communicate, share opinions, and interact with content. These platforms produce vast amounts of unstructured data that, if analyzed correctly, can provide valuable insights into public sentiment. This paper explores the application of Natural Language Processing (NLP) techniques in sentiment analysis of social media data. We discuss various methods such as tokenization, lemmatization, and machine learning algorithms for classification. Sentiment analysis can be used for a wide range of applications, including brand management, political discourse analysis, and customer feedback. The paper reviews key approaches in the field, evaluates challenges faced in the analysis of noisy and complex social media text, and offers potential solutions.
Keywords: Natural Language Processing, Sentiment Analysis, Social Media, Machine Learning, Text Mining, Tokenization, Lemmatization, Deep Learning, Opinion Mining
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