Sentiment Analysis of StockTwits Using Transformer Models

Forecasting stock price movements is an important task for investors and traders, though still very difficult due to the unstable nature and complex behavior of the stock market. Accordingly, each piece of information related to stock prices can be deemed useful. In this regard, social media platforms provide a vast amount of information that can be used to predict stock movements. In this study, we compare the performances of various traditional, deep learning, and state-of-art pre-trained transformer models for text classification of tweets related to the stock market, which are obtained through a financial microblog, StockTwits. For this purpose, we collected 100,000 labeled messages of five stocks, namely, Apple Inc. (AAPL), Amazon (AMZN), Boeing Co, (BA), Walt Disney Co. (DIS), and the SPDR S&P 500 ETF Trust (SPY) for a period between December 2019 and June 2020. We used logistic regression and random forest as traditional classifiers and Long Short Term Memory and Gated Recurrent Unit as the deep learning algorithms, and BERT, DistillBERT, RoBERTa, and XLNet as the state-of-art transformer models to classify tweets as either "bearish" or "bullish" Our numerical study showed that RoBERTa outperformed traditional classifiers and deep learning algorithms in terms of average Flscores.