Financial and Accounting Dictionaries for Text Mining

This research addresses the growing need for specialized lexicons in text mining applications within accounting and finance, driven by the vast amounts of unstructured textual data these fields produce. By conducting a literature review, the study examines the various financial and accounting dictionaries, providing insights into their characteristics and applications while offering guidance on their suitability for tasks such as sentiment analysis and text classification. The study's contribution lies in its synthesis of existing knowledge and identification of gaps regarding accounting and finance lexicons, informing both academic and industry practices. This supports improved decision-making processes in finance, enhancing the precision of text analysis and ensuring the continued relevance of text mining techniques in the dynamic financial domain.

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Published
2024-12-13
DOI
https://doi.org/10.1145/3716554.3716606
Primary Topic
Advanced Text Analysis Techniques
Type
article
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article

Financial and Accounting Dictionaries for Text Mining

Paraskevi Tsoutsa, Panos Fitsilis, Vyron Damasiotis, Konstantinos Solomos
Advanced Text Analysis Techniques
article

Financial and Accounting Dictionaries for Text Mining

Paraskevi Tsoutsa, Panos Fitsilis, Vyron Damasiotis, Konstantinos Solomos
article en

Abstract

This research addresses the growing need for specialized lexicons in text mining applications within accounting and finance, driven by the vast amounts of unstructured textual data these fields produce. By conducting a literature review, the study examines the various financial and accounting dictionaries, providing insights into their characteristics and applications while offering guidance on their suitability for tasks such as sentiment analysis and text classification. The study's contribution lies in its synthesis of existing knowledge and identification of gaps regarding accounting and finance lexicons, informing both academic and industry practices. This supports improved decision-making processes in finance, enhancing the precision of text analysis and ensuring the continued relevance of text mining techniques in the dynamic financial domain.

University of Thessaly (GR)
Openalex Percentile: Top 72%
Advanced Text Analysis Techniques
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