Sentiment analysis of financial stock market news using pre-trained language models

Abstract

Sentiment analysis helps data analysts to find public opinion, actual meaning of the given text (positive meaning, neutral meaning or negative meaning) conduct market research, monitor brand and product reputation, and understand customer experiences of newly introduced items or service. Stock news sentiment analysis is a useful task in the financial domain. However, this is different from the customer feedback for a product or brand, movie review and customer support reviews. This huge difference is because of the domain specific language in stock markets and lack of labeled data. This research implements a stock news sentiment analysis system using the latest transformer-based pre-trained language models in NLP. I could get higher sentiment classification results for the transformer-based pretrained language models than the traditional classifications models in this research. Also

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could reduce classification result bias for the particular stock market specific words, because of the transfer learning method. And I could introduce correlation between stock news sentiment and stock price change percentage value. This proposed model can predict the percentage change value of the stock when received a news.