Twitter-based sentiment analysis (TSA) is a method for automatically processing digital data to extract opinions. This study can offer a plethora of data on consumer perceptions of different products. Yet, a machine would have trouble comprehending the subtleties that individuals can take away from the content on social networks because it is meant for people to read rather than machines. Because of this, the majority of research in this field has historically concentrated on categorizing opinions into one of three primary groups: positive, negative, or neutral. In this research, we examine alternative techniques and emotion models that might assist in teaching computers to recognize the emotions elicited by such confusing utterances. The use of different cutting-edge classifiers, including Naive Bayes and Logistic Regression algorithms that predict outcomes with high accuracy, is suggested in this study. A front end is also created utilizing the Django server.
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