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@article{153106,
        author = {Vaibhav Chavan and Dr. Sanjeev S. Sannakki and Dr. Kuldeep S. Sambrekar},
        title = {Share Market Prediction using Twitter Sentiment Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {5},
        pages = {540-548},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=153106},
        abstract = {We live in the digital era, where the volume of social media activity has reached previously unheard-of proportions in recent years. Twitter is one such prominent online social networking and micro-blogging site, which allows hundreds of millions of people to exchange brief messages in real time about events that deserve widespread notice, as well as to voice public opinion on such events. According to the findings of this study, there is a connection between Twitter sentiment and stock market movement. We are particularly interested in determining if and how effectively sentiment information collected from Twitter can be utilized to forecast future changes in stock prices. Market forecasting is a prominent and significant subject in financial and academic research, as well as in the financial industry. In order to do this job, time series analysis is the most frequent and basic technique that is utilized. The past three months' worth of tweets mentioning a certain businesses are gathered together. The outcome of this experiment demonstrates a statistically significant relationship between changes in daily stock price and changes in polarity of tweets calculated using sentiment analysis of tweets (polarity of tweets).},
        keywords = {Sentiment Analysis, Twitter data, Share market prediction, Data analysis.},
        month = {},
        }