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Does Applying Deep Learning in Financial Sentiment Analysis Lead to Better Classification Performance?

Author

Listed:
  • Cuiyuan Wang

    (CUNY Graduate Center)

  • Tao Wang

    (Queens College and CUNY Graduate Center)

  • Changhe Yuan

    (Queens College and CUNY Graduate Center)

Abstract

Using a unique data set from Seeking Alpha, we compare the deep learning approach with traditional machine learning approaches in classifying financial text. We apply the long short-term memory (LSTM) as the deep learning method and Naive Bayes, SVM, Logistic Regression, XGBoost as the traditional machine learning approaches. The results suggest that the LSTM model outperforms the conventional machine learning methods on all metrics. Based on the t-SNE graph, the success of the LSTM model is partially explained as the high-accuracy LSTM model distinguishes between positive and negative important sentiment words while those words are chosen based on SHAP values and also appear in the widely used financial word dictionary, the Loughran-McDonald Dictionary (2011).

Suggested Citation

  • Cuiyuan Wang & Tao Wang & Changhe Yuan, 2020. "Does Applying Deep Learning in Financial Sentiment Analysis Lead to Better Classification Performance?," Economics Bulletin, AccessEcon, vol. 40(2), pages 1091-1105.
    • Handle: RePEc:ebl:ecbull:eb-19-01019
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2020/Volume40/EB-20-V40-I2-P94.pdf
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    References listed on IDEAS

    as
    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    3. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    4. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    5. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    6. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    7. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Da Wang & YingXue Zhou, 2024. "An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.

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    More about this item

    Keywords

    Machine Learning; Deep Learning; Financial Social Media; Sentiment Analysis; Long Short-Term Memory;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G1 - Financial Economics - - General Financial Markets

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