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Sector-level sentiment analysis with deep learning
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DOI：10.1016/j.knosys.2022.109954.png)
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摘要
En 中文
This paper presents new machine learning methods in the context of natural language processing (NLP) to extract useful information from financial news. Traditional NLP approaches, which are based on the use of lexicons or standard machine learning algorithms, ignore the importance of word position and combinations in texts, thereby resulting in low performance. More recently, NLP empowered by deep learning has achieved remarkable results in various tasks such as sentiment analysis. This paper proposes a deep learning solution for sentiment analysis, which is trained exclusively on financial news and combines multiple recurrent neural networks. Subsequently, our sentiment analysis models are enhanced using a semi-supervised learning method that relies on the detection and correction of presumably mislabeled data. The performance of our proposed solution compared favorably against both traditional and state-of-the-art models based on its performance on previously unseen tweet data. Additionally, this study provides a novel research on the prediction of specific economic sectors affected by news articles. Finally, we propose an ensemble of sentiment and sector models to provide a sector-level sentiment analysis with potential applications in the context of sector fund indices.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Natural language processing
Machine learning
Financial sentiment analysis
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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