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Comparison of the efficiency of Machine Learning algorithms on Twitter Sentiment Analysis of Pathao

Nowadays, we are virtually living on social media, constantly sharing news and opinions regarding things that associate with us. As a result, social media sites are getting flooded with data in the form of opinions, especially on Twitter. However, all these tweets or opinions can be mined and used in various aspects of life and business for the betterment of human beings as a whole. Opinions on social media are unstructured and scattered and needs to be organized properly so that it contains valuable information regarding a specific subject. Sentiment analysis plays a big role here. In this paper, we have presented a way to automatically analyze the sentiment of tweets posted by Pathao (popular ride¬sharing service in South-East Asia) users. We have merged data mining with text mining and computational intelligence to label the tweets as negative or positive. We have used Twitter API to collect dataset from Twitter and followed the supervised learning approach for the development of training corpus. For text classification, we used three different machine learning algorithms-Naive Bayes, Support Vector Machine and Logistic Regression. We also made a comparative analysis of the efficiency of these three machine learning algorithms. Our proposed method is showing effective result and outcomes are satisfactory.