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Singh FINAL 4 25 23 with cert.pdf (929.82 KB)
ETD Abstract Container
Abstract Header
Sentiment Analysis & Time Series Analysis on Stock Market
Author Info
Singh, Aniket Kumar
ORCID® Identifier
http://orcid.org/0009-0008-9189-8528
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1682513807719236
Abstract Details
Year and Degree
2023, Master of Computing and Information Systems, Youngstown State University, Department of Computer Science and Information Systems.
Abstract
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.
Committee
John R. Sullins, PhD (Advisor)
Feng George Yu, PhD (Committee Member)
Alina Lazar, PhD (Committee Member)
Pages
40 p.
Subject Headings
Artificial Intelligence
;
Computer Science
Keywords
Stock Market
;
Sentiment Analysis
;
Opinion Mining
;
Twitter Sentiment Analysis
;
Time Series Analysis
;
Sentiment & Time Series Model
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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)
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Document number:
ysu1682513807719236
Download Count:
1,237
Copyright Info
© 2023, all rights reserved.
This open access ETD is published by Youngstown State University and OhioLINK.