Novel approach of Predicting Human Sentiment using Deep Learning

Abstract

Due to its interactive and real-time character, gathering public opinion through the analysis of massive social data has garnered considerable attention. Recent research have used sentiment analysis and social media to do this in order to follow major events by monitoring people's behavior. In this article, we provide a flexible approach to sentiment analysis that instantly pulls user opinions from social media postings and evaluates them. As time passed, an increasing number of people shared their opinions on social media. More individuals can now communicate with one another as a result. Along with these advantages, it also has certain drawbacks that cause resentment in some people. Hate speech is another possibility. Hate speech impacts the community when it contains insulting or threatening language. Before it spreads, this kind of speech has to be identified and deleted from social media platforms. The process of determining whether a text's feelings reflect hatred or not involves sentiment analysis. Python language was used to analyze the Twitter dataset. There were 5000 Tweets in total in this dataset, and we used deep learning to improve the machine learning model's accuracy. The experimental outcome in both cases of the Twitter dataset uses the Random Forest approach, which has a 99 percent accuracy rate.