  Stock price prediction has always been a difficult task for forecasters.
Using cutting-edge deep learning techniques, stock price prediction based on
investor sentiment extracted from online forums has become feasible. We propose
a novel hybrid deep learning framework for predicting stock prices. The
framework leverages the XLNET model to analyze the sentiment conveyed in user
posts on online forums, combines these sentiments with the post popularity
factor to compute daily group sentiments, and integrates this information with
stock technical indicators into an improved BiLSTM-highway model for stock
price prediction. Through a series of comparative experiments involving four
stocks on the Chinese stock market, it is demonstrated that the hybrid
framework effectively predicts stock prices. This study reveals the necessity
of analyzing investors' textual views for stock price prediction.
