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  Published Paper Details:

  Paper Title

STOCK MARKET ANALYSIS USING MACHINE LEARNING AND SENTIMENT ANALYSIS

  Authors

  Gautami Bafna,  Manish Patil,  Poojan Modi,  Rutika Shinde,  Ashish Awate

  Keywords

machine learning, sentiment analysis, random forest, support vector machine(svm), artificial neural network(ann)

  Abstract


Stock market prediction has always been pivotal and critical task since years, due to vigorous fluctuations in stock values. It helps in determining future stock value for investors, buyers and sellers in commercial market. Looking towards today�s world economy, prediction of stock market has become much more important factor .More accurate and efficient predictions may yield significant profits, stabilize world�s financial condition to some extent. For this we need to build a model for basic stock market on basis of machine learning algorithms and sentiment analysis. We have surveyed eight papers and studied them thoroughly and understood that stock market values can be predicted upto great extent using machine learning by its various algorithms and through sentiment analysis we may get clear idea about whether stock market prices will be high or low. So, in this paper we have use certain machine learning algorithms and sentiment analysis on data retrieved from social media, financial news, historical stock prices and blogs to predict stock market values.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2011060

  Paper ID - 200438

  Page Number(s) - 586-590

  Pubished in - Volume 8 | Issue 11 | November 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Gautami Bafna,  Manish Patil,  Poojan Modi,  Rutika Shinde,  Ashish Awate,   "STOCK MARKET ANALYSIS USING MACHINE LEARNING AND SENTIMENT ANALYSIS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 11, pp.586-590, November 2020, Available at :http://www.ijcrt.org/papers/IJCRT2011060.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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