Technology advancement has network social media day-to-day expansion on the internet. Reviews, forums conversations, blogging, tweeting, remarks, and comments on social networking sites are all examples of people have used the internet to communicate their ideas and opinions. Twitter, one of the most popular micro-blogging platforms are where people communicate their thoughts as tweets, making it one of the best sources for sentimental analysis. Twitter’s sentiment categorization procedure includes polarity analysis of the tweets emotions. This approach presents, Sentiment Classification Analysis of Tweets on Twitter Data using Machine Learning algorithm. The opinions expressed in tweets on Twitter are analyzed using feature selection for each score word. The characteristics of words are trained and tested using a Bayes Classifier (NBC), which is also used to forecast the sentiment orientation of each tweet. According to experimental findings, described methodology provides excellent categorization outcomes in terms of F1-Score, precision, recall, and accuracy