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Harvesting social media sentiment analysis to enhance stock market prediction using deep learning
Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053016/ https://www.ncbi.nlm.nih.gov/pubmed/33954250 http://dx.doi.org/10.7717/peerj-cs.476 |
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author | Mehta, Pooja Pandya, Sharnil Kotecha, Ketan |
author_facet | Mehta, Pooja Pandya, Sharnil Kotecha, Ketan |
author_sort | Mehta, Pooja |
collection | PubMed |
description | Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology. |
format | Online Article Text |
id | pubmed-8053016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80530162021-05-04 Harvesting social media sentiment analysis to enhance stock market prediction using deep learning Mehta, Pooja Pandya, Sharnil Kotecha, Ketan PeerJ Comput Sci Artificial Intelligence Information gathering has become an integral part of assessing people’s behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public’s views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data mining techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company’s stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naïve Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology. PeerJ Inc. 2021-04-13 /pmc/articles/PMC8053016/ /pubmed/33954250 http://dx.doi.org/10.7717/peerj-cs.476 Text en © 2021 Mehta et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Mehta, Pooja Pandya, Sharnil Kotecha, Ketan Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title | Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title_full | Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title_fullStr | Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title_full_unstemmed | Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title_short | Harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
title_sort | harvesting social media sentiment analysis to enhance stock market prediction using deep learning |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053016/ https://www.ncbi.nlm.nih.gov/pubmed/33954250 http://dx.doi.org/10.7717/peerj-cs.476 |
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