Sentimental Analysis of Twitter Data using Machine Learning Algorithms

As social media is becoming a necessity for communication, a lot of data is available on this platform, which could be helpful for analysis. At a certain time, we use Twitter to tweet almost about similar topics, different emotions. In this article, sentimental analysis is proposed to get an idea about what people have in their minds or get people’s emotions. It segregates every tweet to its appropriate emotion. The emotion might be either positive or negative. The proposed methodology has two steps, namely preprocessing and classification. The corpus is created after all necessary preprocessing. The classification algorithms such as Logistic Regression, Linear SVC, Random Forest Classifier, Bernoulli NB, Decision Tree Classifier, Voting Classifier, and KNN Classifier are used for classification. Twitter 2020 and 2021 data has been taken for experimentation. The performance of Linear SVC shows a higher accuracy on training data, and Linear Regression shows higher accuracy in testing data.