Millions of people are connected to one another in the modern digital world through social media and the internet. The entire globe is embracing innovation. Each year brings new breakthroughs due to technical advancements. Twitter and Emotion Recognition are a couple of these developments. Twitter is used to get opinions about trends, goods, etc. On the social networking site, Twitter, individuals may utilize text messages and GIFs to share their thoughts and opinions about actual events. Written texts are the most popular means for individuals to interact with one another and share their ideas. In this research, we offer a sentiment analysis system that uses an ensemble of classifiers to automatically recognize emotions in text. Text is one of the most popular means of human expression of feelings and ideas, especially on social networking sites. Deep Learning and AI are used in previous methodologies. We are putting out a methodology that uses machine learning to identify the emotion based on user opinions. Opinion mining and sentiment analysis in machine learning are useful tools for analyzing people's feelings, attitudes, and views about various topics without requiring human tweet reading. The emotions may be predicted using the vote classifier algorithm. All feelings are divided into three categories: “Positive,” “Negative,” and “Neutral” We are giving a comment in addition to the degree of emotion.