Sentiment Analysis (SA), also called Opinion Mining, is currently one of the most studied research fields. It aims to analyze people¿s sentiments, opinions, attitudes, emotions, etc., In this project, we basically carry out Sentiment Analysis on Twitter data. Sentiment analysis has been handled as a Natural Language Processing (NLP) task at many levels of granularity for text analysis and Machine Learning (ML) techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase. We used the TextBlob library to do an analysis of all tweets as positive, negative and neutral. We have used the Support Vector Machine (SVM) algorithm to train the model and compare performance with some other popular classifier.