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

Cuiyuan Wang (), Tao Wang () and Changhe Yuan ()
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Cuiyuan Wang: CUNY Graduate Center
Tao Wang: Queens College and CUNY Graduate Center
Changhe Yuan: Queens College and CUNY Graduate Center

Economics Bulletin, 2020, vol. 40, issue 2, 1091-1105

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).

Keywords: Machine Learning; Deep Learning; Financial Social Media; Sentiment Analysis; Long Short-Term Memory (search for similar items in EconPapers)
JEL-codes: C1 G1 (search for similar items in EconPapers)
Date: 2020-04-29
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