Towards Machine Learning Based Text Categorization in the Financial Domain Article Swipe
Frederic Voigt
,
José Miranda
,
Keshav Dahal
,
Qi Wang
,
Kai von Luck
,
Peer Stelldinger
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/citds62610.2024.10791384
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/citds62610.2024.10791384
Despite the widespread research on text categorization in various Natural Language Processing (NLP) domains, there exists a noticeable void concerning its application to financial data. This study addresses this gap by employing pre-trained Bidirectional Encoder Representations from Trans-formers (BERT) models, fine-tuned specifically for the financial domain, to categorize newspaper articles focusing on financial topics. This is the first time that the dataset presented in this paper has been used. Further we evaluate the efficacy of established models in sentiment prediction using these rather long texts. Finally, we delve into the intricacies of company-specific sentiment and relevance prediction within these articles, acknowl-edging the prevalence of multiple companies being mentioned in one article, thus contributing to a more nuanced understanding of text analysis in the financial sector.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/citds62610.2024.10791384
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- DOI
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https://doi.org/10.1109/citds62610.2024.10791384Digital Object Identifier
- Title
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Towards Machine Learning Based Text Categorization in the Financial DomainWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-26Full publication date if available
- Authors
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Frederic Voigt, José Miranda, Keshav Dahal, Qi Wang, Kai von Luck, Peer StelldingerList of authors in order
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https://doi.org/10.1109/citds62610.2024.10791384Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://research-portal.uws.ac.uk/en/publications/22e29d03-5c9d-4e30-8dc5-fe85e20e6d6eDirect OA link when available
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Categorization, Computer science, Domain (mathematical analysis), Artificial intelligence, Text categorization, Natural language processing, Machine learning, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4400037165, https://openalex.org/W2195027038, https://openalex.org/W2911971986, https://openalex.org/W4298110867, https://openalex.org/W3043424630, https://openalex.org/W6739901393, https://openalex.org/W6697136110, https://openalex.org/W3139035125, https://openalex.org/W2753259282, https://openalex.org/W2038466413, https://openalex.org/W1991018417, https://openalex.org/W1546425147, https://openalex.org/W4393153055, https://openalex.org/W3196503391, https://openalex.org/W2973750772, https://openalex.org/W4283791273, https://openalex.org/W4387373347, https://openalex.org/W4372324631, https://openalex.org/W4315853251, https://openalex.org/W2980708516, https://openalex.org/W6780227653, https://openalex.org/W6767182473, https://openalex.org/W3035101152, https://openalex.org/W6752926069, https://openalex.org/W2963691377, https://openalex.org/W2616247523, https://openalex.org/W4230405732, https://openalex.org/W4287073370, https://openalex.org/W1484391353, https://openalex.org/W4389115219, https://openalex.org/W4365799947, https://openalex.org/W3102564565, https://openalex.org/W4385245566, https://openalex.org/W2913602408, https://openalex.org/W3037252472, https://openalex.org/W4300485781 |
| referenced_works_count | 36 |
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