Abstract
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.
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This work was supported by the National Natural Science Fund (71701099) and National Key Research and Development Program of China (2018YFC0830400).
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Xiaofeng, W., Jinghua, Z., Chenxi, J. et al. Research on sentiment classification of futures predictive texts based on BERT. Computing (2021). https://doi.org/10.1007/s00607-021-00989-9
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DOI: https://doi.org/10.1007/s00607-021-00989-9