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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
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    References listed on IDEAS

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    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.
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    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.

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