In recent years, the data growth has suddenly increased through social media like Twitter, Facebook, YouTube, etc., because everyone has used. These unstructured data is used to handle various applications in data analytics. These applications are used for public opinion and sentiment analysis (POSA) in Twitter by various Machine Learning (ML) Algorithms. In this paper, mainly discuss about Twitter sentiment analysis and Machine Learning Algorithms. We take the sample tweets from Twitter, and finding the positive, negative, and neutral words, and then will make it polarity score by using Twitter Sentiment analysis. Using this data are applying ML algorithms. This algorithm is used to show the comparison result between Random Forest (RF) algorithm and Classification algorithm to know which one is best performance. Random Forest algorithm is good when compare with Classification algorithm. Classification algorithm is best for easy understanding. Finally in this social media have low level of security in the Twitter data. © 2019 Elsevier B.V., All rights reserved.