Word synonym relationships for text analysis: A graph-based approach.

Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and...

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Autor principal:	Hend Alrasheed
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1c65899f3c8d438cb9be4094cd2a0b33
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id	oai:doaj.org-article:1c65899f3c8d438cb9be4094cd2a0b33
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spelling	oai:doaj.org-article:1c65899f3c8d438cb9be4094cd2a0b332021-12-02T20:06:22ZWord synonym relationships for text analysis: A graph-based approach.1932-620310.1371/journal.pone.0255127https://doaj.org/article/1c65899f3c8d438cb9be4094cd2a0b332021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255127https://doaj.org/toc/1932-6203Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.Hend AlrasheedPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255127 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hend Alrasheed Word synonym relationships for text analysis: A graph-based approach.
description	Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.
format	article
author	Hend Alrasheed
author_facet	Hend Alrasheed
author_sort	Hend Alrasheed
title	Word synonym relationships for text analysis: A graph-based approach.
title_short	Word synonym relationships for text analysis: A graph-based approach.
title_full	Word synonym relationships for text analysis: A graph-based approach.
title_fullStr	Word synonym relationships for text analysis: A graph-based approach.
title_full_unstemmed	Word synonym relationships for text analysis: A graph-based approach.
title_sort	word synonym relationships for text analysis: a graph-based approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1c65899f3c8d438cb9be4094cd2a0b33
work_keys_str_mv	AT hendalrasheed wordsynonymrelationshipsfortextanalysisagraphbasedapproach
_version_	1718375348300677120

Word synonym relationships for text analysis: A graph-based approach.

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