Author Name
Aastha Chourasiya, Aasma Khan, Kashish Bajaj, Muskan Tomar, Tarun Kohli, Dipti Chauhan
Abstract
Sentiment analysis and emotion detection form an important branch of Natural Language Processing that deals with identifying and classifying emotions, attitudes, or opinions within a text dataset. The concepts of sentiment analysis and emotion detection, their application, and their challenges are studied. In this paper, we have discussed different approaches related to sentiment analysis, like the machine learning models of VADER, Roberta, Naive Bayes, SVM, etc. We also discussed the difficulties that arise due to ambiguity, context, sarcasm, irony, and cultural and linguistic variability. Our study indicates how NLP, machine learning, and cognitive science should proceed with continuous research and development to develop more accurate analysis tools.