www.rspsciencehub.com Volume 02 Issue 12S December 2020 International Research Journal on Advanced Science Hub (IRJASH) 56 Special Issue of First International Conference on Science, Technology & Management (ICSTM-2020) Sentiment Analysis of Twitter Data Sanjay Rai 1 , S. B. Goyal 2 ,Jugnesh Kumar 3 1 Student,City University, MALASIA 2 Director, Faculty of Information Technology,City University, MALASIA, 3 Director, SAITM, INDIA drsbgoyal@gmail.com 2 Abstract The World Wide Web has taken seriously new ways for individuals to convey their views and conclusions on different topics, models and issues. Clients create content that resides in a variety of media, such as web gathering, conversation gathering, and weblogs, and provide a solid and generous foundation for gaining momentum in different areas such as advertising and research. Policy, logic research, market forecasts and business outlook. Hypothesis research extracts inferences from information available online and orders the emotions that the author conveys for a particular item into up to three predefined categories (good, negative, and unbiased). Identify the problem. This article outlines a hypothesis review cycle for quickly ordering unstructured news on Twitter. In addition, we are exploring different ways to perform a detailed emotional survey on Twitter News. In addition, it presents a parametric correlation of strategies considered according to recognized boundaries. This work tends to make the case enjoy investigating on Twitter; The values communicated in them represent the tweets: positive, negative or fair. Twitter is an online thumbnail that contributes to a blog and a wide range of interactions, allowing customers to create short 140-character short instructions. It is a fast growing association with more than 200 million subscribers, of which 100 million are dynamic customers and half of them constantly sign up for Twitter, generating around 250 million tweets every day. Due to this overwhelming use, we plan to achieve a biased impression of the public by breaking the estimates communicated in the tweets. Researching public opinion is important for some applications, for example, when companies are looking to respond to their material, predict political careers, and anticipate economic wonders like stock trading. The function of this to build a useful classifier for the command in a precise and programmed way of the stream of fuzzy tweets. Keywords: Twitter, Social media, Machine learning, Sentiment analysis, Natural language processing, Mining, Part of speech, Bigram 1. Introduction Micro blogging websites have been evolving for becoming a source of data and information. This is due to the nature of microblogs, where individuals constantly post their feelings on a range of topics, investigating current issues and complaints, and sharing their guesses about everyday objects. The various number of people have been utilizing social media like Facebook, Instagram and Twitter. Online media can help you create large amounts of information such as tweets, status, remark, blog entries, surveys, and more. Different associations are looking for extracting data from twitter for analysing sentiments of the user over Twitter. This study will help counter the difficulties of apathetic investigation on Twitter, which may be associated with the characterization of tweets based on reliability, denial, and impartiality. The active users of Twitter has been huge all over the world. The test will focus on