Sentiment analysis pacts with recognising and organising views and opinions articulated in the original text. Social media generates a massive expanse of data in updates, tweets, and sentiment-rich posts. Sentiment study of this data created by the users is vital in recognising the overall judgement of the userbase, analysing conversations, and sharing views which can be implemented in determining commercial tactics, political studies, and calculating community activities. Twitter sentiment analysis is tougher than overall sentiment examination due to the prevalence of misspellings, dialect words, symbols, and emoticons. This paper presents the analysis of the Twitter posts of a particular account using Python alongside Machine Learning. By carrying out a sentiment study in one specific area, it is likely to classify the consequence of that area’s data in sentiment cataloguing. This paper presents a feature for organising a user’s most recent tweets and visualising them using graphs, charts, and word clouds.