Aspect-based sentiment analysis for social media text using NLP and Deep Learning

Participatory moments on social media platforms increasingly add up to something more substantial. Communicating our thoughts and feelings about the book through shared observations, appraisals, and illustrative examples. For instance, the data posted on social media platforms like Twitter can be mined for insights into users' values, beliefs, and emotions. The author's perspective can be better understood through the lens of sentiment analysis. Almost all studies of social media's massive user base have looked at how users' sentiments can be broken down into positive, negative, and neutral categories. In this project, we've set out to define the phrases in terms of four distinct emotional states: joy, rage, fear, and melancholy. There have been a lot of approaches implemented in the field of dynamic textual sentiment recognition in the event of further interactions, but not nearly enough of them were based on intensive training. In this research, we elaborate on a game-changing deep learning-based method (RNN+LSTM) for dealing with a variety of problems associated with emotion distribution by making use of informative data. We present a novel method for translating it to a binary distribution and a standard machine-learning classification problem, and we employ a comprehensive knowledge technique to settle the reconstructed matter. In terms of classification accuracy, our hybrid approach will prevail over more conventional ml methods.