Sentiment analysis of users in the financial market using the BERT algorithm in the New York Stock market - Heidari, Nima; Mirzamohammadi, Saeed; Amiri, Babak (Management Strategies and Engineering Sciences, 2026)
Nowadays, analyzing individuals’ sentiments is supposedly a main indicator in financial management and economic forecasts. Human analysis of different opinions and news during exchanging different opinions between individuals can take several minutes, and investors in financial markets need to make decisions quickly. Such challenging scenarios require faster ways for investors to make decisions. The current research analyzes the sentiments of users in the financial market using the BERT algorithm in the New York stock market based on profit news. This research has set up a data set based on the BERT model to evaluate and predict changes in New York stock market prices for Tesla and has analyzed the results through Python software. This research introduced two multi-class and multi-label models based on individuals’ opinions to classify data on users' sentiments. The results showed that the multi-label model without considering the time lag with an F1 score equal to 0.846 has better performance compared to the multi-class models.