The fluctuation of the stock market greatly depends on investors’ sentiment-based factors. Sentiment analysis of investors’ reviews on financial exchange web platforms such as Guba stock forum (guba.com.cn), can help stockholders to understand the stock market more effectively. However, due to the unavailability of high-quality labeled datasets and deficient features of stock comments extracted by a single model, the accuracy of the existing sentiment methods still requires further improvements. This paper proposes a method that utilizes the FinBERT-CNN-based sentiment model for Guba comments. The semantic features of Guba comments are extracted by using the FinBERT pre-training model. Meanwhile, a convolution neural network (CNN) is applied to learn the local features of Guba comments. It enables the proposed method to learn features more precisely and improve the emotion classification’s accuracy significantly. Experiments show that the proposed method outperforms the existing models. Furthermore, the correlation analysis on Guba comments and stock market data demonstrates a relationship between the investors’ emotions and stock market volatility.