Hybrid self-optimized clustering model based on citation links and textual features to detect research topics

Abstract

The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify “core documents”. First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers.

Extractive multi-document summarization using multilayer networks

“…In recent years, a new class of methods based on network theory have been proposed to analyze texts. Applications of network models in text analysis include the study of scientific documents [5,6,7,8], stylometry [9,10,11,12], sense discrimination [13,14] and several other applications [15,16]. The problem of creating single-document extractive summaries has benefited from these previous network models of texts [17,18,19].…”

Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature

“…The clusters of DEA concepts identified in this study differ from those of Yu et al (2017). This is not surprising since these researchers use a different methodology:…”

“…first clustering papers, not keywords, and then identifying the main DEA topics studied in the papers of each cluster. Yu et al (2017) acknowledge that the clusters they found are mostly related to the applications of DEA in different sectors. Nevertheless, there are certain similarities between the communities shown above and the clusters in Yu et al (2017).…”

“…Yu et al (2017) acknowledge that the clusters they found are mostly related to the applications of DEA in different sectors. Nevertheless, there are certain similarities between the communities shown above and the clusters in Yu et al (2017). Thus, for example, the community we have labeled "Operations Research and Statistics keywords" is almost identical to their "Cluster#1: Decision making analysis and fuzzy DEA".…”

“…and using regression analysis to test these factors in explaining their brokerage capabilities in diffusing knowledge across regions and continents. More recently, Yu et al (2017) have carried out a clustering of the DEA literature using a similarity coefficient based on co-citation and co-word analysis, identifying from the title and abstract of the papers that belong to each cluster the main research topics addressed in those papers.…”