The efficient use of text data is very important in investor sentiment research and other fields. Through the sentiment classification of text data containing investor sentiment, we can effectively and accurately identify the sentiment contained in the text. This paper takes the futures market forecast text published by 21 futures companies as the data source and constructs a sentiment classification model of the market forecast text based on BERT (Bidirectional Encoder Representations from Transformers) according to the characteristics of the market forecast text. The sentiment classification of the market forecast text is carried out by using the sentiment classification model of the market forecast text based on BERT and a classification model based on the classical classification algorithm. The classification effects of different models are compared. The results show that the optimized BERT model has the best classification effect. This enriches the research methods of investor sentiment measurement in the financial field and improves the accuracy of this kind of sentiment measurement result.

文本数据的有效利用在投资者情绪研究等领域非常重要。通过对包含投资者情绪的文本数据进行情绪分类，我们可以有效准确地识别文本中包含的情绪。本文以21家期货公司发布的期货市场预测文本为数据源，根据市场预测文本的特点，构建了基于BERT（Bidirectional Encoder Representations from Transformers）的市场预测文本情感分类模型。利用基于BERT的市场预测文本情感分类模型和基于经典分类算法的分类模型对市场预测文本进行情感分类。比较了不同模型的分类效果。结果表明，优化后的BERT模型具有最好的分类效果。这丰富了金融领域投资者情绪度量的研究方法，提高了这种情绪度量结果的准确性。