Title
Citeopinion: Evidence-Based Evaluation Tool For Academic Contributions Of Research Papers Based On Citing Sentences
Abstract
Purpose: To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers.Design/methodology/approach: CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers; it starts with an analysis on the citing sentences, then it identifies major academic contribution points of the cited paper, positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves (problems, methods, conclusions, etc.), and sentiment analysis and topic clustering.Findings: Citing sentences in a citing paper contain substantial evidences useful for academic evaluation. They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation, beyond simple citation statistics.Practical implications: The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers, research teams, and institutions.Originality/value: No other similar practical tool is found in papers retrieved.Research limitations: There are difficulties in acquiring full text of citing papers. There is a need to refine the calculation based on the sentiment scores of citing sentences. Currently, the tool is only used for academic contribution evaluation, while its value in policy studies, technical application, and promotion of science is not yet tested.
Year
DOI
Venue
2019
10.2478/jdis-2019-0019
JOURNAL OF DATA AND INFORMATION SCIENCE
Keywords
DocType
Volume
Cited paper, Citing paper, Citing sentence, Citation motive, Citation sentiment, Academic contribution, Evaluation
Journal
4
Issue
ISSN
Citations 
4
2096-157X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Xiaoqiu Le100.34
Jingdan Chu200.34
Siyi Deng300.34
Qihang Jiao400.34
Jingjing Pei500.34
Liya Zhu600.34
Junliang Yao700.34