基于Transformer编码器的金融文本情感分析方法

doi:10.16180/j.cnki.issn1007-7820.2020.09.002

Abstract

Abstract:

Sentiment analysis plays an important role in many fields, but most of these studies focus on the field of commodity reviews and microblogs, and lack of sentiment analysis of financial texts. To solve this problem, a financial text sentiment analysis method based on Transformer encoder is presented in this paper. Transformer encoder is a feature extraction unit based on the self-attention mechanism. When Transformer encoder processes text sequence information, it can link any two words in a sentence without distance restriction, which overcomes the problem of long-term dependencies. The multi-head attention mechanism is used to calculate the same sentence several times and capture more semantic features implied in the context. The experiment is car+ried out on a balanced corpus data set based on financial news. The experimental results show that the method based on Transformer encoder has best effect in the field of financial text sentiment analysis compared with the models based on convolution neural network and recurrent neural network.

Key words: sentiment analysis, finance, self-attention mechanism, transformer encoder, scaled dot-product attention, multi-head attention

CLC Number:

TP391

LI Fupeng,FU Dongxiang. Sentiment Analysis Method of Financial Text Based on Transformer Encoder[J].Electronic Science and Technology, 2020, 33(9): 10-15.

Figures/Tables 8

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References 17

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标签	评价	内容
1	费用率影响净利润释放,长期估值仍具优势	中国天楹(000035)公司公告,15年前三季度实现收入4.6亿,同比增长61.31%,实现扣非净利润1.17亿,同比增长18.97%,并预告2015年全年增长26.85%~43.99%。投资要点:费用率持续攀升导致利润释放低于预期,明年一季度有望缓解。
0	宝钢净利创18年以来新低	宝钢净利创18年以来新低净利下滑超八成,公司董事、总经理戴志浩薪酬降幅达88.6%,巨额汇兑损失成业绩下滑主因之一新京报讯宝钢股份3月31日发布2015年年报显示,公司2015年1月~12月实现净利润10.13亿元,创下18年以来的新低,同比上一年度也大幅下降82.51%。

参数	数值
嵌入维度	200
学习率	0.001
多头注意力的Dropout	0.900
Sentence Length	400
Batch Size	200

模型	precision	recall	F₁
textCNN	0.968 63	0.925 83	0.946 75
CRNN	0.950 05	0.944 25	0.947 14
Bi-LSTM	0.975 67	0.863 48	0.916 16
Transformer	0.961 80	0.978 50	0.970 10

Sentiment Analysis Method of Financial Text Based on Transformer Encoder

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