Sentiment analysis of Twitter texts using Machine learning algorithms

Abstract

Since the two last decades social media networks have become a part of our daily life. Today, getting information from social media, tracking trends in social media, learning the feelings and emotions of people on social media is very essential. In this study, sentiment analysis was performed on Twitter text to learn about the subjective polarities of the writings. The polarities are positive, negative, and neutral. At the first stage of the sentiment analysis a public data set has been obtained. Secondly, natural language processing techniques have been applied to make the data ready for machine learning training procedures. Lastly sentiment analysis is performed by using three different machine learning algorithms. We reached 89% accuracy with Support Vector Machines, 88% accuracy with Random Forest, and 72% accuracy with Gaussian Naive Bayes classifier.