Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting

被引:2
|
作者
Liapis, Charalampos M. [1 ]
Kotsiantis, Sotiris [1 ]
机构
[1] Univ Patras, Dept Math, Patras 26504, Greece
关键词
financial forecasting; multivariate forecasting; temporal convolutional networks; BERT; emotion classification; multi-label classification; feature selection; ensembles; sentiment analysis; SENTIMENT ANALYSIS;
D O I
10.3390/info14110596
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given the above, in this work, a multivariate forecasting methodology incorporating temporal convolutional networks in combination with a BERT-based multi-label emotion classification procedure and correlation feature selection is proposed. The results from an extensive set of experiments, which included predictions of three different time frames and various multivariate ensemble schemes that capture 28 different types of emotion-relative information, are presented. It is shown that the proposed methodology exhibits universal predominance regarding aggregate performance over six different metrics, outperforming all the compared schemes, including a multitude of individual and ensemble methods, both in terms of aggregate average scores and Friedman rankings. Moreover, the results strongly indicate that the use of emotion-related features has beneficial effects on the derived forecasts.
引用
收藏
页数:20
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