Text mining arXiv: a look through quantitative finance papers

Fuente: arXiv
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1. Verfasser: Bianchi, Michele Leonardo
Format: Preprint
Veröffentlicht: 2024
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author Bianchi, Michele Leonardo
author_facet Bianchi, Michele Leonardo
contents This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. We extract and analyze crucial information from the entire documents, including the references, to understand the topics trends over time and to find out the most cited researchers and journals on this domain. Additionally, we compare numerous algorithms to perform topic modeling, including state-of-the-art approaches.
format Preprint
id arxiv_https___arxiv_org_abs_2401_01751
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Text mining arXiv: a look through quantitative finance papers
Bianchi, Michele Leonardo
Digital Libraries
Information Retrieval
General Finance
This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we examine the contents of quantitative finance papers posted in arXiv from 1997 to 2022. We extract and analyze crucial information from the entire documents, including the references, to understand the topics trends over time and to find out the most cited researchers and journals on this domain. Additionally, we compare numerous algorithms to perform topic modeling, including state-of-the-art approaches.
title Text mining arXiv: a look through quantitative finance papers
topic Digital Libraries
Information Retrieval
General Finance
url https://arxiv.org/abs/2401.01751