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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
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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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