Real-Time Twitter Sentiment Analytics and Visualization Using Vader

Twitter is an excellent source of real-time user-generated data. This is a platform where anyone may use Tweets to share their own views, opinions, or feedback on various topics. These themes can be from any field, including politics, business, and entertainment. Sentiment Analysis is a technique of obtaining key consumer data from text in the form of a sentiment score, and it identifies the emotional tone behind a body of text. If this information can be obtained and assessed accurately, and can be used as a vital element in making a decision. However, due to the changing and complex formats of tweets, analyzing tweets has been more challenging. With sentiment values in mind, there is a huge amount of use of emoticons and slang. In this paper, we intend to provide visual demonstration of real-time tweets by classifying and determining the polarity score of live tweets where it is positive, negative, or neutral using Vader, that is more efficient than traditional algorithms, and providing the user with a graph of the subject matter, which is updated in accordance with the tweets posted every second, trying to make decision-making a very simple process by providing information in a word cloud