Deep Learning-based Sentiment Analysis on Social Media

Understanding public sentiment on social media is essential for businesses, researchers, and organizations to track trends, monitor brand reputation, and make datadriven decisions. This study explores sentiment analysis using a combination of machine learning and deep learning techniques, focusing on NLP classifiers, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models. By analyzing tweets, posts, and comments, we classify sentiments as positive, negative, or neutral. NLP techniques like tokenization, stopword removal, and vectorization help preprocess the text, making it suitable for model training. Our results show that while traditional models provide decent accuracy, deep learning models, especially LSTMs, excel at capturing context and long-term dependencies in text. This research highlights the power of combining NLP with deep learning to improve sentiment analysis accuracy, providing valuable insights for businesses, social research, and customer engagement strategies.