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Deep-Learning-Based Financial Message Sentiment Classification in Business Management
A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Spe...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313961/ https://www.ncbi.nlm.nih.gov/pubmed/35898776 http://dx.doi.org/10.1155/2022/3888675 |
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author | Shao, Chen Chen, Xiaochen |
author_facet | Shao, Chen Chen, Xiaochen |
author_sort | Shao, Chen |
collection | PubMed |
description | A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Specifically, in the classification process, the seq2seq model is firstly adopted to extract the abstract from the financial message, which can reduce the influence of invalid information and speed up processing. In the process of sentiment classification, a bidirectional LSTM model is adopted for classification, which can more comprehensively make use of context information. Experiments are carried out to testify the proposed method through the open-source data set. It can be seen that the proposed method can effectively transfer from the reduced Amazon data set to the StockTwits financial text data set. Compared with the parameter-frozen-based method and the SDA-based method, the recognition rates have improved by 0.5% and 6.8%, respectively. If the target domain data set can be directly adopted for training, the recognition rate of the proposed method is higher than that of the SVM method and the LSTM method by 8.3% and 4.5%, respectively. |
format | Online Article Text |
id | pubmed-9313961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93139612022-07-26 Deep-Learning-Based Financial Message Sentiment Classification in Business Management Shao, Chen Chen, Xiaochen Comput Intell Neurosci Research Article A deep-learning-based financial text sentiment classification method is proposed in this paper, which can provide a reference for business management. In the proposed method, domain adaptation is adopted to solve the common problem of insufficient labeled samples in the financial textual domain. Specifically, in the classification process, the seq2seq model is firstly adopted to extract the abstract from the financial message, which can reduce the influence of invalid information and speed up processing. In the process of sentiment classification, a bidirectional LSTM model is adopted for classification, which can more comprehensively make use of context information. Experiments are carried out to testify the proposed method through the open-source data set. It can be seen that the proposed method can effectively transfer from the reduced Amazon data set to the StockTwits financial text data set. Compared with the parameter-frozen-based method and the SDA-based method, the recognition rates have improved by 0.5% and 6.8%, respectively. If the target domain data set can be directly adopted for training, the recognition rate of the proposed method is higher than that of the SVM method and the LSTM method by 8.3% and 4.5%, respectively. Hindawi 2022-07-18 /pmc/articles/PMC9313961/ /pubmed/35898776 http://dx.doi.org/10.1155/2022/3888675 Text en Copyright © 2022 Chen Shao and Xiaochen Chen. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Shao, Chen Chen, Xiaochen Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title | Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title_full | Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title_fullStr | Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title_full_unstemmed | Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title_short | Deep-Learning-Based Financial Message Sentiment Classification in Business Management |
title_sort | deep-learning-based financial message sentiment classification in business management |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313961/ https://www.ncbi.nlm.nih.gov/pubmed/35898776 http://dx.doi.org/10.1155/2022/3888675 |
work_keys_str_mv | AT shaochen deeplearningbasedfinancialmessagesentimentclassificationinbusinessmanagement AT chenxiaochen deeplearningbasedfinancialmessagesentimentclassificationinbusinessmanagement |