A Real-Time Opinion Mining of Trending Twitter Topics Using NLP: Leveraging Social Media for Dynamic Sentiment Insights

This paper presents a Real-Time Opinion Mining of Trending Twitter Topics Using NLP: Leveraging Social Media for Dynamic Sentiment Insights using state-of-the-art NLP models. Tweets are fetched via Twitter API v2 using Tweepy with advanced query filtering to ensure linguistic relevance and diversity. Sentiment classification is performed using the BERTbased model `nlptown/bert-base-multilingual-uncased-sentiment`, mapping tweets to a 1 (extremely negative)–5 (extremely positive) star scale. Data visualization through bar graphs, pie charts, boxplots, and word clouds reveals key public opinion patterns. Additionally, tweets are grouped by sentiment and summarized using Facebook’s `bart-large-cnn` model. The system enables dynamic extraction of insights for trending topics, integrating sentiment mining, engagement analysis, and abstractive summarization.