摘要：Using a unique data set from Seeking Alpha, we compare the deep learning approach with traditional machine learningapproaches in classifying financial text. We apply the long short-term memory (LSTM) as the deep learning methodand Naive Bayes, SVM, Logistic Regression, XGBoost as the traditional machine learning approaches. The resultssuggest 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 distinguishesbetween positive and negative important sentiment words while those words are chosen based on SHAP values andalso appear in the widely used financial word dictionary, the Loughran-McDonald Dictionary (2011).
关键词：Machine Learning; Deep Learning; Financial Social Media; Sentiment Analysis; Long Short-Term Memory
