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Sentiment Analysis & Time Series Analysis on Stock Market

Abstract Details

2023, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
Investors are always looking for ways to make profit in the stock market. Predicting this highly volatile market has been historically challenging. This study explores the use of the social media platform, Twitter, and Machine Learning Algorithm for Time Series Analysis. Our findings suggested that Twitter’s data may not be the best for Sentiment Analysis, while other machine learning techniques for Time Series Analysis such as LSTM would be effective. This could potentially help an investor with higher returns.
John R. Sullins, PhD (Advisor)
Feng George Yu, PhD (Committee Member)
Alina Lazar, PhD (Committee Member)
40 p.

Recommended Citations

Citations

  • Singh, A. K. (2023). Sentiment Analysis & Time Series Analysis on Stock Market [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1682513807719236

    APA Style (7th edition)

  • Singh, Aniket. Sentiment Analysis & Time Series Analysis on Stock Market. 2023. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1682513807719236.

    MLA Style (8th edition)

  • Singh, Aniket. "Sentiment Analysis & Time Series Analysis on Stock Market." Master's thesis, Youngstown State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1682513807719236

    Chicago Manual of Style (17th edition)