Tweet Sentiment & Emotion Analysis

Abstract

Abstract: Tweet Sentiment & Emotion Analysis using Bi-LSTM in RNN. Twitter has developed into a useful medium for sharing ideas, attitudes, and feelings. Applications like opinion mining, market research, and social trend analysis all depend on the sentiment and emotion of tweets. The Bi-LSTM architecture in RNN (Recurrent Neural Networks) is used in this study to present an advanced method for sentiment and emotion analysis on Twitter. By utilising machine learning techniques, the objective is to increase the precision and efficacy of mood and emotion analysis. The study emphasises both conventional text analysis and real-time data analysis. Organisations and governmental bodies may continuously monitor sentiment and emotional patterns on Twitter thanks to real-time analysis, which enables them to react quickly to emerging problems or crises. In a typical text analysis, historical tweet data is examined to learn more about user viewpoints, emotional patterns, and sentiment distributions. The Bi-LSTM architecture is used because it can effectively capture the context and sequential dependencies found in tweets. To ensure consistent analysis, the system gathers real-time tweets and conducts the preprocessing stages. Monitoring sentiment changes, emotional responses, and new trends on Twitter are all made possible by this study. The goal of the research is to improve the precision and efficacy of sentiment and emotion analysis on Twitter by utilising machine learning techniques, real-time data analysis, and standard text analysis. The findings and conclusions will aid in understanding public mood and feelings in the digital age.