Finding and labelling expressions of opinion or emotion in written content is the primary focus of sentiment analysis. Now more than ever, people turn to online communities to share their perspectives and vent their emotions. As a result, there is a mountain of data being produced each day that may be successfully mined for insights. Conducting sentiment analysis on this kind of data can help generate a holistic perspective on certain items. Sentiment research on Twitter can be difficult because of the widespread use of slang and misspellings. The rapid influx of fresh words also makes it harder to analyse and compute the sentiment than it would be with more conventional sentiment analysis methods. The maximum number of characters allowed in a tweet is 140. Therefore, another challenge is overcoming the limitations of short communications to learn crucial details. Sentiment analysis of tweets can greatly benefit from knowledge-based techniques and machine learning. As individuals adapt to new ways of interacting on social media platforms like Snapchat, Instagram, Twitter, etc., the amount of data they generate increases at an exponential rate. There are literally billions of fresh pieces of content uploaded every day, including text, music, and video. This is due to the fact that numerous people frequent the website in question. These individuals wish to express their views on whatever subject they feel fits. These entries are meant to express the thoughts of a single individual on a particular subject. The goal of this research is to analyze these posts and identify the feelings that motivated their creation. We’ve settled on Twitter as the venue for this effort. The changes made to this social networking site are referred to as “tweets.” In this research, we look at how Twitter users feel about specific businesses. Critical feedback on the company’s products from people all around the world would be offered by generating a basic sentiment score and then classifying it as positive or negative.