INTERNATIONAL JOURNAL OF PROGRESSIVE RESEARCH IN SCIENCE AND ENGINEERING, VOL.2, NO.7, JULY 2021. SHUBHAM RAJ., et.al: STOCK MARKET PREDICTION USING SENTIMENT ANALYSIS 72 Stock Market Prediction Using Sentiment Analysis Shubham Raj 1 , Sindhu Yadav 1 , Md. Meraj Alam 1 , Vijay Kumar 1 , Pruthvi P R 2 1 Students, Dept. of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India. 2 Assistant Professor, Dept. of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India. Corresponding Author: shubhamrajbest@gmail.com Abstract: - Social sites like Twitter help millions of people to share their thoughts about the Stock market and what they feel about them. The tweet may be a short and easy sort of expression. Detecting sentiments in-text features a wide selection of applications including identifying anxiety or depression of people and measuring the well-being or mood of a community. Therefore, during this review paper, we focused on Sentiment Analysis of Twitter data. Sentiments are often expressed in some ways, which will be seen like countenance and gestures, speech, and transcription. Sentiment Analysis in text documents is actually a content-based classification problem involving concepts from the domains of tongue processing also as Machine Learning. Using different aspects, the research of Sentiment Analysis of Twitter Data is often performed. We can see the various sorts of Sentiment Analysis and techniques want to perform the extraction of the info. In this paper, we have taken a comparative study of various approaches and techniques of sentiment analysis having Twitter as knowledge. Key Words: — Stock Market Prediction, Machine Learning, Sentiment Analysis, Twitter API. I. INTRODUCTION The most dynamic and advanced means of doing business is the stock market, commonly known as the stock exchange. It is a complex model for little companies, investors and therefore the banking sector to all or any generate revenue and minimize risks. This paper would however plan to use open-source datasets and current data to predict future exchange rates employing a machine-learning algorithm. In the course of years, the share market has been a crucial a part of the expansion of the many companies also as of a country's GDP. In the financial markets of the worldwide private sector, stock markets are given the foremost important position in economic liberalization. There have been a number of flexible impacts on stock markets, the most essential of which are historical data. Many approaches for forecasting stock related data were developed using different techniques and models, which used traditional prices, past revenue growth and dividends, so we all know that we'd like data alongside one among the above factors to effectively predict stocks, in order that the effective market hypothesis are often built. In this paper, the Twitter Application Programming Interface (Twitter API), which offers a streaming API, has been taken under consideration within the study of monetary data and continually returns the info. Each data collected reflects the user's status or attitude with reference to a selected subject. This is available between a basic HTTP documentation and a twitter account. After all data is composed for each line, an interpretation is initiated of the emotions relevant to every tweet then a mood is predicted which features a direct impact on the stock status. Sentiment analysis is essentially a drag of classification during which the info content is categorized with a positive or negative opinion. Various models are developed supported various learning algorithms used for the training results. The flowing data is collected through the flowing API after such a model is developed. II. LITERATURE SURVEY Twitter may be a popular social networking website where users post and interact with messages referred to as “tweets”. This is a way for people to precise their thoughts or feelings about different subjects. Various different parties like consumers and marketers have done sentiment analysis on such tweets to collect insights into products or to conduct marketing research. With the recent advancements in machine learning algorithms, the accuracy of our sentiment analysis predictions is in a position to enhance. During this report, we had planned Manuscript revised July 16, 2021; accepted July 17, 2021. Date of publication July 18, 2021. This paper available online at www.ijprse.com ISSN (Online): 2582-7898