Deep Learning Approaches for Sentiment Analysis on Financial Microblog Dataset

Abstract

Sentiment analysis of financial news and social media messages along with movement of stock prices could aid in improving the forecasting accuracy of stock prices. In this regard, we aim to perform sentiment analysis of a financial microblog, namely, StockTwits. We carried out the analysis on labelled messages of twelve stocks for a period of five months ranging from May 2019 to September 2019 using various Deep Learning (DL) approaches. We compared the performance of the DL classifiers with traditional machine learning approaches. Long Short Term Memory (LSTM) model and its variations such as bidirectional LSTM and bidrirectional LSTM with dropout outperformed other classifiers. Though use of dropout mechanism did not improve the performance of the model but there was a decrease in bias and variance. Further, we evaluated the performance of various optimizers such as rmsprop, adam, adagrad, adamax and nadam on LSTM. The success rate of all optimizers was similar.