Analysis of Twitter Sentiments using Machine Learning Algorithms

There exists plenty of data which prevails on the web available for internet users at the time of development of various web technologies. The internet has now grown into a place for online education, sharing ideas of each people and also for sharing one’s opinion, the internet sites for instance google+, Facebook, Twitter are growing like a wind because they allow users in expressing their persuasion freely on diverse topics. It also acknowledges users to get into a conversation with other people or groups, and also share their messages across the world. The perusal on sentiment analysis in twitter data has driven a lot of people. It is helpful for classifying the tweets depending upon the polarity, which is positive negative and neutral. Analyzing the twitter data is tough because it has a large amount of data as the twitter contains data on diverse topics. There arises the need for simplified computing Machine learning algorithms. Therefore, for our Sentimental tweet prediction, supervised classification algorithms have been used. Using distinctive supervised machine learning algorithms such as K-Nearest Neighbor(KNN), Naïve Bayes, Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest algorithms were employed for analyzing the sentiment of tweets. These machine learning algorithms has been compared, and the most effective method is chosen to predict the outcome.