Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach
Resonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level...
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doaj-e01211d739214aa4bd6db33fa87656842020-11-25T02:07:05ZengElsevierHeliyon2405-84402018-06-0146e00659Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approachTai Wang0Zongkui Zhou1Xiangen Hu2Zhi Liu3Yi Ding4Zhiqiang Cai5National Engineering Research Center for E-learning, Central China Normal University, Wuhan, 430079, China; Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; Corresponding author.Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; School of Psychology, Central China Normal University, Wuhan, 430079, ChinaKey Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, China; School of Psychology, Central China Normal University, Wuhan, 430079, China; Department of Psychology, University of Memphis, TN, 38152, USANational Engineering Research Center for E-learning, Central China Normal University, Wuhan, 430079, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430079, ChinaDepartment of Psychology, University of Memphis, TN, 38152, USAResonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level. To make up the missing link between word and document level resonances, we devoted our contributions to topic resonances in a novel and natural setting: academic commentaries. Ninety-three academic commentaries from ninety-three authors, along with their references and original papers, are analyzed by a latent Dirichlet allocation based natural language processing approach. This approach can decompose a corpus written and read by an author into several topics with different weights, which can reveal the phenomena ignored at word or document level. We found that (1) topic resonances commonly exist between commenters' fundamental input and output topics; (2) output words are re-allocated by commenters to echo salient input topics; (3) commenters are more prone to associate references which focus on the non-dominant input topics; and (4) topic resonance can even be predicted by a Hebbian-like model which matches the aforementioned findings. These findings will continue to enrich our understanding on the relationship among probe, feedback and context.http://www.sciencedirect.com/science/article/pii/S2405844018302822Information scienceLinguisticsPsychology |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tai Wang Zongkui Zhou Xiangen Hu Zhi Liu Yi Ding Zhiqiang Cai |
spellingShingle |
Tai Wang Zongkui Zhou Xiangen Hu Zhi Liu Yi Ding Zhiqiang Cai Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach Heliyon Information science Linguistics Psychology |
author_facet |
Tai Wang Zongkui Zhou Xiangen Hu Zhi Liu Yi Ding Zhiqiang Cai |
author_sort |
Tai Wang |
title |
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
title_short |
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
title_full |
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
title_fullStr |
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
title_full_unstemmed |
Latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
title_sort |
latent topics resonance in scientific literature and commentaries: evidences from natural language processing approach |
publisher |
Elsevier |
series |
Heliyon |
issn |
2405-8440 |
publishDate |
2018-06-01 |
description |
Resonance is generally used as a metaphor to describe the manner how the information from different sources is combined. Although it is an attractive and fundamental phenomenon in human behavior studies, most studies observed semantic resonances in well-controlled experimental settings at word level. To make up the missing link between word and document level resonances, we devoted our contributions to topic resonances in a novel and natural setting: academic commentaries. Ninety-three academic commentaries from ninety-three authors, along with their references and original papers, are analyzed by a latent Dirichlet allocation based natural language processing approach. This approach can decompose a corpus written and read by an author into several topics with different weights, which can reveal the phenomena ignored at word or document level. We found that (1) topic resonances commonly exist between commenters' fundamental input and output topics; (2) output words are re-allocated by commenters to echo salient input topics; (3) commenters are more prone to associate references which focus on the non-dominant input topics; and (4) topic resonance can even be predicted by a Hebbian-like model which matches the aforementioned findings. These findings will continue to enrich our understanding on the relationship among probe, feedback and context. |
topic |
Information science Linguistics Psychology |
url |
http://www.sciencedirect.com/science/article/pii/S2405844018302822 |
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