Lightweight Scheme to Capture Stock Market Sentiment on Social Media Using Sparse Attention Mechanism: A Case Study on Twitter

Abstract

Over through the years, people have invested in stock markets in order to maximize their profit from the money they possess. Financial sentiment analysis is an important topic in stock market businesses since it helps investors to understand the overall sentiment towards a company and the stock market, which helps them make better investment decisions. Recent studies show that stock sentiment has strong correlations with the stock market, and we can effectively monitor public sentiment towards the stock market by leveraging social media data. Consequently, it is crucial to develop a model capable of reliably and quickly capturing the sentiment of the stock market. In this paper, we propose a novel and effective sequence-to-sequence transformer model, optimized using a sparse attention mechanism, for financial sentiment analysis. This approach enables investors to understand the overall sentiment towards a company and the stock market, thereby aiding in better investment decisions. Our model is trained on a corpus of financial news items to predict sentiment scores for financial companies. When benchmarked against other models like CNN, LSTM, and BERT, our model is “lightweight” and achieves a competitive latency of 10.3 ms and a reduced computational complexity of 3.2 GFLOPS—which is faster than BERT’s 12.5 ms while maintaining higher computational complexity. This research has the potential to significantly inform decision making in the financial sector.