A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Iqbal, Sehrish; Hassan, Saeed‐Ul; Aljohani, Naif Radi; Alelyani, Salem; Nawaz, Raheel; Bornmann, Lutz

doi:10.1007/s11192-021-04055-1

Cited by 33 publications

(12 citation statements)

References 119 publications

(207 reference statements)

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“…Typically, scientometrics is concerned with monitoring research, evaluating the scientific contribution of authors, journals, and specific works, and assessing the dissemination of scientific knowledge [20]. As part of these approaches, researchers develop methodological principles for deriving information from communication activities and use specific methods to achieve these goals, including citation analysis, social network analysis, syndicated terminology analysis, and text mining [16]. Scientometric studies usually focus on authorship or measurement of journal or professional association contributions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recently, there has been a resurgence of interest in applying machine learning methods to scientometric classification and analysis of scientific literature, as these algorithms have achieved acceptable results in text analysis and classification. Commonly used machine learning algorithms include support vector machines (SVM), Naive Bayes classifiers, and the K-nearest neighbor model (KNN) [16]. However, with the abundance and diversity of scientific research and the exponential increase in scientific output, classification methods based on general scientometric information and traditional machine learning methods have shown significant shortcomings in terms of coarse classification and insufficient accuracy [1,9,17].…”

Section: Introductionmentioning

confidence: 99%

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Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

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With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

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“…Over recent years, the increase in social media and e-commerce websites have initiated a paradigm shift in recommender system research towards transformer-based approaches that utilize NLP tasks to extract users’ preferential information [ 52 – 56 ]. For instance, Aipe et al .…”

Section: Related Workmentioning

confidence: 99%

A hybrid group-based movie recommendation framework with overlapping memberships

et al. 2022

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Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure.

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“…Studies have suggested various windows of text to capture the extent of the contribution of the cited publication in the citing publications for citation context identi cation. In the review of the citation context window size by Iqbal et al, (2021), it was revealed that a range of one to four sentence-window and 50 to 100 wordwindow were recommended in the previous studies for citation context identi cation.…”

Section: Citation Context Paradigmmentioning

confidence: 99%

A Proposed Method for Residual Citation Allocation Based on Citation Contexts’ Similarity

Asubiaro

Ajiferuke

2021

Preprint

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This article proposes an approach for allocating residual citations to scientific publications and demonstrating this proposed approach with a sample of biomedical publications. Residue citations (i.e., citations that are lost due to citation practices termed “Obliteration by Incorporation” and the “Palimpsestic Syndrome”) in consequent citations in the second, third or nth generations are then reconstituted. The proposed approach takes into account citation contexts (i.e., the contribution of a cited publication) for allocating residual citation. The proposed method for allocating residual citation is based on the similarity between the citation contexts of a publication and those of its nth generation citations in their n+1th generation citations. The proposed method was demonstrated using a sample with ten base articles and their five generations of citations, from which 5,272 citation context pairs were obtained. The proposed indirect citation weighting was compared with the existing cascading citation weighting method using one T-test. Statistical tests were also performed to understand the differences in the residual citations from one generation to the other. Like the cascading citation system, residual citations from articles to their generations of citations decreased as the number of generations increased. However, residue citations accrued to publications at all the generations were statistically different between the proposed residual citation and the cascading citation system. This study proposes a method for assessing scientific communication based on the contribution of scientific publications beyond the conventional direct citation.

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A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Abstract: A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies. Scientometrics.

Cited by 33 publications

References 119 publications

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

A hybrid group-based movie recommendation framework with overlapping memberships

A Proposed Method for Residual Citation Allocation Based on Citation Contexts’ Similarity

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