Real-Time Social Media Sentiment Prediction Using BERT, CNN, and LSTM Techniques

The proliferation of social platforms has made increasingly significant to understand and analyze public sentiment in real time. This paper presents a comprehensive study on real-time sentiment scrutiny, specifically focusing on data derived from Twitter. By leveraging a dataset from Kaggle, we develop and evaluate three distinct models. Each model is competent and evaluated to gauge its effectiveness in processing and classifying real-time social media data. Through rigorous evaluation, we compare these models across multiple performance metrics, identifying the remarkably adequate accession. The results offer valuable insights into the practical application of these methods, suggesting thoroughfare for future exploration and enhancements in the fields of sentiment analysis and natural language processing.