Data Science in Decision-Making Processes:  A Scientometric Analysis

Gryncewicz, Wiesława; Sitarska-Buba, Monika

doi:10.35808/ersj/2558

Cited by 1 publication

(1 citation statement)

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“…Finally, typical terms describing the literature topics were included in the feature term lexicon in coordination with the knowledge of subject matter experts. Validation of terms for inclusion in the final lexicon was performed using two methods: validation by three subject matter experts and comparison with existing terms from the data science and analytics literature [4,30,41,42]. The final validated lexicon of feature terms will be used in downstream literature classification tasks by helping to select appropriate data science terms from publication platforms/databases (e.g., ProQuest, EBSCOhost, and Scopus) and pre-annotating these terms to support the development of a deep-learning application for classifying mentions of data science and analytics in the literature.…”

Section: Building Stop and Feature Terms Lexiconsmentioning

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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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: Building Stop and Feature Terms Lexiconsmentioning

confidence: 99%