IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2022i1p5-d1012495.html
   My bibliography  Save this article

Using Social Media & Sentiment Analysis to Make Investment Decisions

Author

Listed:
  • Ben Hasselgren

    (School of Computing, Engineering and the Build Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

  • Christos Chrysoulas

    (School of Computing, Engineering and the Build Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

  • Nikolaos Pitropakis

    (School of Computing, Engineering and the Build Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

  • William J. Buchanan

    (School of Computing, Engineering and the Build Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK)

Abstract

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation.

Suggested Citation

  • Ben Hasselgren & Christos Chrysoulas & Nikolaos Pitropakis & William J. Buchanan, 2022. "Using Social Media & Sentiment Analysis to Make Investment Decisions," Future Internet, MDPI, vol. 15(1), pages 1-23, December.
    • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:5-:d:1012495
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/1/5/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/1/5/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    2. Xi Zhang & Jiawei Shi & Di Wang & Binxing Fang, 2018. "Exploiting Investors Social Network for Stock Prediction in China's Market," Papers 1801.00597, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sihan Wu & Fuyu Gu, 2023. "Lightweight Scheme to Capture Stock Market Sentiment on Social Media Using Sparse Attention Mechanism: A Case Study on Twitter," JRFM, MDPI, vol. 16(10), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
    2. Béatrice BOULU-RESHEF & Catherine BRUNEAU & Maxime NICOLAS & Thomas RENAULT, 2022. "An Experimental Analysis of Investor Sentiment," LEO Working Papers / DR LEO 2940, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    3. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Md Shamim Hossain & Mst Farjana Rahman, 2023. "Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches," FIIB Business Review, , vol. 12(4), pages 386-402, December.
    5. Yongan Xu & Jianqiong Wang & Zhonglu Chen & Chao Liang, 2023. "Sentiment indices and stock returns: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1063-1080, January.
    6. Mazzotta, Stefano, 2022. "Immigration narrative sentiment from TV news and the stock market," Journal of Behavioral and Experimental Finance, Elsevier, vol. 34(C).
    7. Feng Wang & Wei Chai & Xiaotian Shi & Mingru Dong & Bin Yan, 2021. "Does Regional Financial Resource Contribute to Economic Growth? From the Perspective of Spatial Correlation Network," SAGE Open, , vol. 11(1), pages 21582440219, March.
    8. Wenbo Ge & Pooia Lalbakhsh & Leigh Isai & Artem Lensky & Hanna Suominen, 2023. "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data," Papers 2306.12446, arXiv.org, revised Jun 2023.
    9. Ying Wang & Hongwei Zhang & Wang Gao & Cai Yang, 2023. "Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains," Tourism Economics, , vol. 29(2), pages 460-487, March.
    10. Bowden, James & Gemayel, Roland, 2022. "Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    11. Afanasyev, Dmitriy O. & Fedorova, Elena & Ledyaeva, Svetlana, 2021. "Strength of words: Donald Trump's tweets, sanctions and Russia's ruble," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 253-277.
    12. Weiguo Zhang & Xue Gong & Chao Wang & Xin Ye, 2021. "Predicting stock market volatility based on textual sentiment: A nonlinear analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1479-1500, December.
    13. Nicolas, Maxime L.D., 2022. "Estimating a model of herding behavior on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    14. Andrew Todd & James Bowden & Yashar Moshfeghi, 2024. "Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(1), March.
    15. Dmitry G. Rodionov & Polina A. Pashinina & Evgenii A. Konnikov & Olga A. Konnikova, 2022. "Information Environment Quantifiers as Investment Analysis Basis," Economies, MDPI, vol. 10(10), pages 1-16, September.
    16. Wenwen Liu & Jinyu Yang & Jingrui Chen & Lei Xu, 2023. "How Social-Network Attention and Sentiment of Investors Affect Commodity Futures Market Returns: New Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    17. Yajie Qi & Huajiao Li & Sui Guo & Sida Feng, 2019. "Dynamic Transmission of Correlation between Investor Attention and Stock Price: Evidence from China’s Energy Industry Typical Stocks," Complexity, Hindawi, vol. 2019, pages 1-15, December.
    18. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    19. Bouteska, Ahmed & Hajek, Petr & Abedin, Mohammad Zoynul & Dong, Yizhe, 2023. "Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:5-:d:1012495. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.