Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms
Suvarna G. Kanakaraddi, Ashok K. Chikaraddi, Karuna C. Gull, Prakash S. Hiremath · 2020
Today's advancement in the design of web technology, has made enormous data available for internet operators and also the numerous data is being created Web technology networking sites like Twitter, Facebook, Google+ has become a platform for sharing and exchanging the knowledge, for discussing and expressing their views about different themes with, unlike groups. The proposed system emphasizes largely on opinion investigation of twitter facts that support the investigation of the data in the tweets that show ideas are extremely unstructured, varied and in some instances positive or negative, and also emotions are taken into account for the sentiment analysis. The proposed system provides a performance analysis of machine learning algorithms such as Support Vector Machine, Navie Bayes, Max Entropy, LSTM, CNN, Random forest. Among all these techniques SVM provides an accuracy of 79.90%