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

•We leverage a method, named MTL, to classify transdisciplinary CA and CS based on their unique and shared features. Compared to the single-task learning (STL) approach, MTL uses an extra shared encoder to improve classification performance.•We evaluate the performance of our methodology in different disciplines and compare its performance to those of other models. The results indicate that our model achieves a better F1-score and has higher recognition ability compared to other models. In addition, our model aims to provide a decision support system for detecting the fine-grained CA and CS of transdisciplinary citation.•To the best of our knowledge, we are the first to conduct a fine-grained transdisciplinary citation analysis that combines CAC and CSC based on the large-scale Chinese transdisciplinary citation corpus of information science cited by other disciplines from CSSCI, extending the transdisciplinary impact analysis dimension of Chinese information science and improving the effective generalization of the proposed MTL model. 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