Sentiment analysis and classification of Indian farmers’ protest using twitter data

Protests are an integral part of democracy and an important source for citizens to convey their demands and/or dissatisfaction to the government. As citizens become more aware of their rights, there has been an increasing number of protests all over the world for various reasons. With the advancement of technology, there has also been an exponential rise in the use of social media to exchange information and ideas. In this research, we gathered data from the microblogging website Twitter concerning farmers’ protest to understand the sentiments that the public shared on an international level. We used models to categorize and analyze the sentiments based on a collection of around 20,000 tweets on the protest. We conducted our analysis using Bag of Words and TF-IDF and discovered that Bag of Words performed better than TF-IDF. In addition, we also used Naive Bayes, Decision Trees, Random Forests, and Support Vector Machines and also discovered that Random Forest had the highest classification accuracy.