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Transdisciplinary fine-grained citation content analysis: A multi-task learning perspective for citation aspect and sentiment classification

Author

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  • Kong, Ling
  • Zhang, Wei
  • Hu, Haotian
  • Liang, Zhu
  • Han, Yonggang
  • Wang, Dongbo
  • Song, Min

Abstract

The diffusion of citation knowledge is an important measure of transdisciplinary scientific impact and the diversity of transdisciplinary citation content (sentences). Moreover, combining citation sentiment (CS) and citation aspect (CA) can help researchers identify the attitudes, ideas, or positions reflected in the evolution of scientific elements (e.g., theories, techniques, and methods). This is because of their use by scholars from different disciplines, paving the way toward transdisciplinary penetration and the development of domain knowledge through the proliferation of cited knowledge. However, most studies mainly address citation aspect classification (CAC) and citation sentiment classification (CSC) separately, ignoring their shared features of interactions. In this study, we construct a dataset for transdisciplinary citation content analysis using citations and academic full texts from the Chinese Social Sciences Citation Index (CSSCI), which includes 14,832 manually-annotated citations. Thereafter, we utilized the developed dataset to conduct a transdisciplinary fine-grained citation content analysis by combining CAC and CSC. The objective of the CAC task was to classify transdisciplinary citations into theoretical concepts (TC), methodological techniques (MT), and data information (DI), whereas the CSC task classified citations into positive, negative, and neutral classes. Furthermore, we leveraged a multi-task learning (MTL) model to perform CAC and CSC jointly and then compared its performance to those of several widely-used deep learning models. Our model achieved 83.10 % accuracy for CAC and 80.46 % accuracy for CSC, demonstrating its superiority to single-task systems. This indicates the strong correlation between the CAC and CSC of transdisciplinary citation tasks, benefiting from each other when learned concurrently. This new method can be used as an auxiliary decision support system to extend the analysis of transdisciplinary citation content.

Suggested Citation

  • Kong, Ling & Zhang, Wei & Hu, Haotian & Liang, Zhu & Han, Yonggang & Wang, Dongbo & Song, Min, 2024. "Transdisciplinary fine-grained citation content analysis: A multi-task learning perspective for citation aspect and sentiment classification," Journal of Informetrics, Elsevier, vol. 18(3).
    • Handle: RePEc:eee:infome:v:18:y:2024:i:3:s1751157724000555
    DOI: 10.1016/j.joi.2024.101542
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    References listed on IDEAS

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