The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

Zhu, Haoran; Lei, Lei

doi:10.1177/21582440221089963

Cited by 6 publications

(3 citation statements)

References 113 publications

(124 reference statements)

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“…Essentially this is a quantitative method to depict publication patterns within a given field based on a body of literature. There are many bibliometric studies on natural and social sciences in general ( Hsu and Ho, 2014 ; Zhu and Lei, 2022 ) and on various specific disciplines such as management sciences ( Liao et al, 2018 ), biomass research ( Chen and Ho, 2015 ), computer sciences ( Xie and Willett, 2013 ), and sport sciences ( Mancebo et al, 2013 ; Ríos et al, 2013 ), etc. In these studies, researchers tracked developments, weighed research impacts, and highlighted emerging scientific fronts with bibliometric methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Trends and hot topics in linguistics studies from 2011 to 2021: A bibliometric analysis of highly cited papers

Song

Zhang²

2023

Front. Psychol.

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High citations most often characterize quality research that reflects the foci of the discipline. This study aims to spotlight the most recent hot topics and the trends looming from the highly cited papers (HCPs) in Web of Science category of linguistics and language & linguistics with bibliometric analysis. The bibliometric information of the 143 HCPs based on Essential Citation Indicators was retrieved and used to identify and analyze influential contributors at the levels of journals, authors, and countries. The most frequently explored topics were identified by corpus analysis and manual checking. The retrieved topics can be grouped into five general categories: multilingual-related, language teaching, and learning related, psycho/pathological/cognitive linguistics-related, methods and tools-related, and others. Topics such as bi/multilingual(ism), translanguaging, language/writing development, models, emotions, foreign language enjoyment (FLE), cognition, anxiety are among the most frequently explored. Multilingual and positive trends are discerned from the investigated HCPs. The findings inform linguistic researchers of the publication characteristics of the HCPs in the linguistics field and help them pinpoint the research trends and directions to exert their efforts in future studies.

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Section: Literature Reviewmentioning

confidence: 99%

Trends and hot topics in linguistics studies from 2011 to 2021: A bibliometric analysis of highly cited papers

Song

Zhang²

2023

Front. Psychol.

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“…The clinical text classification process involves different tasks that may differ from system to another. For example, clinical text classification may provide smoking-status detection, patient classifications for different purposes, and classification of obesity [3][4]. EMR are widely adapted in medical providers' systems, which led to creating large containers of structured and unstructured clinical data.…”

Section: Introductionmentioning

confidence: 99%

Clinical Text Classification with Word Representation Features and Machine Learning Algorithms

Almazaydeh¹,

Abuhelaleh²,

Tawil³

et al. 2023

Int. J. Onl. Eng.

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Clinical text classification of electronic medical records is a challenging task. Existing electronic records suffer from irrelevant text, misspellings, semantic ambiguity, and abbreviations. The approach reported in this paper elaborates on machine learning techniques to develop an intelligent framework for classification of the medical transcription dataset. The proposed approach is based on four main phases: the text preprocessing phase, word representation phase, features reduction phase and classification phase. We have used four machine learning algorithms, support vector machines, naïve bayes, logistic regression and k-nearest neighbors in combination with different word representation models. We have applied the four algorithms to the bag of words, to TF-IDF, to word2vec. Experimental results were evaluated based on precision, recall, accuracy and F1 score. The best results were obtained with the combination of the k-NN classifier, and the word represented by Word2vec achieving an accuracy of 92% to correctly classify the medical specialties based on the transcription text.

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“…Bibliometric analysis is a field of study that attempts to use bibliographic data extracted from past publications and their citation relations to evaluate and reveal the structure of research. Previous research [18][19][20][21] used bibliometric methods to analyse different subfields of computer science. The primary objective of this study is to unveil the structure and dynamics of hate speech detection studies, guided by the following four research questions: (a) What is the relative importance of articles from journals and conferences in addressing the problem of social media hate speech detection?…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection

Mutanga,

Olugbara,

Naicker

2023

J. Inf. Syst. Informatics

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Social media has become an important web technology for creating and sharing information plus enhancing business reputations worldwide. However, the anonymity accorded by social media platforms has been cryptically vituperated to spread horrendous content such as hate speech. Recently, researchers have been progressively gravitating towards the use of deep learning techniques to address the problem of social media hate speech detection. This study provides bibliometric analysis and mapping of the existing literature on hate speech detection using deep learning algorithms. The study used articles published between 2016 and 2022 from the Scopus database, while Vos Viewer, Biblioshiny, and Panda’s software tools were employed for the bibliometric analysis. The research explored the yearly trajectory of recent publications, dominant countries, collaborative institutions, sources of primary studies that have employed deep learning for hate speech detection, and the intellectual and social structures of the research constituents. It has been observed that the literature on hate speech detection is rapidly growing, but research output and collaborations from the developing countries of the world are still limited. The findings of this study provide insights into the intellectual structure and advancements in deep learning applications for hate speech detection while identifying research gaps for future work.

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The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

Cited by 6 publications

References 113 publications

Trends and hot topics in linguistics studies from 2011 to 2021: A bibliometric analysis of highly cited papers

Trends and hot topics in linguistics studies from 2011 to 2021: A bibliometric analysis of highly cited papers

Clinical Text Classification with Word Representation Features and Machine Learning Algorithms

Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection

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