Sentiment analysis of social media text using natural language processing

This research approach explores the effectiveness and efficiency of Natural Language Processing (NLP), machine learning, and deep learning systems for sentiment analysis of social media text. The research focuses on identifying positive, negative, and neutral sentiment from the texts that users post online for communication, interaction, and reaction. Several traditional machine learning models, including Support Vector Machine (SVM), Logistic Regression, and Naïve Bayes, were comparatively evaluated alongside advanced deep learning architectures such as CNN, LSTM, and BERT. The research findings indicate that transformer-based and deep learning models achieve comparatively higher classification accuracy because of their stronger contextual understanding and semantic interpretation capabilities. The study also highlights the significance of preprocessing methods, including handling emojis, hashtags, abbreviations, and noisy textual structures, in improving prediction performance. Overall, the research contributes to the growing field of NLP-driven sentiment analysis by integrating comparative computational frameworks into a unified analytical framework.