The People Moods Analysing Using Tweets Data on Primary Things with the Help of Advanced Techniques

Researchers studying sentiment analysis are interested in Twitter, a major social media platform. Text mining research on Twitter Sentiment Analysis (TSA) is growing. Using computers, “TSA” processes subjective Twitter data like opinions and sentiments. Social media platforms like Twitter have many customer reviews. Traditional customer feedback methods like focus groups, business surveys, and questionnaires take time and resources. However, Twitter reviews lack structure, making manual analysis difficult and time-consuming. Using machine learning and deep learning for sentiment analysis yields poor classification accuracy. This study uses optimized deep learning and machine learning models and clever SNS scraper-extracted tweets. Experimental results show that the proposed method can classify three emotions with 99% accuracy. A hybrid classifier (HC) with LSTM and Bi-GRU classifies. When Bi-GRU and LSTM are averaged, effective output is obtained. The proposed Hosted Cuckoo Optimisation Algorithm fine-tunes LSTM weight to improve detection accuracy.