A novel emerging topic detection method: A knowledge ecology perspective

2022 ◽  
Vol 59 (2) ◽  
pp. 102843
Author(s):  
Jinqing Yang ◽  
Wei Lu ◽  
Jiming Hu ◽  
Shengzhi Huang
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eymen Çağatay Bilge ◽  
Hakan Yaman

PurposeThis study aims to identify the trends that have changed in the field of construction management over the last 20 years.Design/methodology/approachIn this study, 3,335 journal articles published in the years 2000–2020 were collected from the Web of Science database in construction management. The authors applied bibliometric analysis first and then detected topics with the latent Dirichlet allocation (LDA) topic detection method.FindingsIn this context, 20 clusters from cluster analysis were found and the topics were extracted in clusters with the LDA topic detection method. The results show “building information modeling” and “information management” are the most studied subjects, even though they have emerged in the last 15 years “building information modeling,” “information management,” “scheduling and cost optimization,” “lean construction,” “agile approach” and “megaprojects” are the trend topics in the construction management literature.Research limitations/implicationsThis study uses bibliometric analysis. The authors accept that the co-citation and co-authorship relationship in the data is ethical. They accept that honorary authorship, self-citation or honorary citation do not change the pattern of the construction management research domain.Originality/valueThere has been no study conducted in the last 20 years to examine research trends in construction management. Although bibliometric analysis, systematic literature reviews and text mining methods are used separately as a methodology for extracting research trends, no study has used enhanced bibliometric analysis and the LDA topic detection text mining method.


2018 ◽  
Vol 70 ◽  
pp. 1010-1023 ◽  
Author(s):  
Wei Ai ◽  
Kenli Li ◽  
Keqin Li

2014 ◽  
Vol 602-605 ◽  
pp. 2174-2179
Author(s):  
Chuan Zhu Liao ◽  
Wei Wang ◽  
Ming Yan Jiang

With the characteristics of timeliness, rapid spread and easy access, micro-blog has made it possible to timely unearth emergencies and dynamically track the latest development. Considering frequency and timeliness of the words as well as the influence of resources, this paper proposes a sudden topic detection method, especially for micro-blog, and establishes a context-based weight evaluation model. Compared with semantic similarity model, this context-based model is especially more adaptable to micro-blog. Real experiment data from micro-blog proves that the proposed methods could detect sudden incidents effectively with big data processing capacity and low time complexity.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Zheng Xie

Abstract Purpose We proposed a method to represent scientific papers by a complex network, which combines the approaches of neural and complex networks. Design/methodology/approach Its novelty is representing a paper by a word branch, which carries the sequential structure of words in sentences. The branches are generated by the attention mechanism in deep learning models. We connected those branches at the positions of their common words to generate networks, called word-attention networks, and then detect their communities, defined as topics. Findings Those detected topics can carry the sequential structure of words in sentences, represent the intra- and inter-sentential dependencies among words, and reveal the roles of words playing in them by network indexes. Research limitations The parameter setting of our method may depend on practical data. Thus it needs human experience to find proper settings. Practical implications Our method is applied to the papers of the PNAS, where the discipline designations provided by authors are used as the golden labels of papers’ topics. Originality/value This empirical study shows that the proposed method outperforms the Latent Dirichlet Allocation and is more stable.


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