Defining Interdisciplinarity through Topic Modelling Technique: An AI-Driven Approach
Description
nterdisciplinary studies or research is a method of integrating knowledge and
perspective from multiple disciplines to address complex problems.
Interdisciplinarity consists of collaboration and cooperation from different fields
to assemble together diverse perspectives and approaches in order to find
innovative solutions. In this paper, we have measured interdisciplinarity using
topic modelling approach. Topic modelling is an unsupervised natural language
processing (NLP) technique for creating structured data from a collection of
unstructured textual corpus without predefined training data. This text mining
method have discovered hidden semantic patterns demonstrated by a text
corpus to produce cluster of words, called “topics”.
In this article, we have collected data from The Lens database (www.lens.org) and then applied topic
modeling approach using the tool Coconut Libtool (www.coconut-libtool.com/). The final output is shown
by network graphs, sunburst visualization, chord diagram etc. The main objective of the paper is to define
the perspective of interdisciplinarity through topic model techniques.
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References
- Evans, E. D. (2016). Measuring Interdisciplinarity https://journals.sagepub.com/doi/full/10.1177/2378023116654147 Kherwa, P., Using Text. & Bansal, P. (2018). (PDF) Topic Modeling: A Comprehensive Review. https://www.researchgate.net/publication/334667298_Topic_Modeling_A_Comprehensive_Review Kim, K., Kogler, D. F., & Maliphol, S. (2024). (PDF) Identifying interdisciplinary emergence in the science of science: Combination of network analysis and BERTopic. ResearchGate. https://doi.org/10.1057/s41599-024-03044-y Laureate, C. D. P., Buntine, W., & Linger, H. (2023). A systematic review of the use of topic models for short text social media analysis. Artificial Intelligence Review, 56(12), 14223–14255. Paul, M., & Girju, R. (2012). (PDF) Topic modeling of research fields: An interdisciplinary perspective. https://www.researchgate.net/publication/228346424_Topic_modeling_of_research_fields_An_interdisc iplinary_perspective Saha, B. (2021). Application of topic modelling for literature review in management research. Interdisciplinary Research in Technology and Management, 249–256. Santosa, F. A., Lamba, M., George, C., & Downie, J. S. (2024). Coconut Libtool: Bridging Textual Analysis Gaps for Non-Programmers. Proceedings of the Association for Information Science and Technology, 61(1), 639 644. https://doi.org/10.1002/pra2.1072 Singh, S., Singh, S., & Dhir, S. (2023). The evolving relationship of entrepreneurship, technology, and innovation: A topic modeling perspective. The International Journal of Entrepreneurship and Innovation, 14657503231179597. https://doi.org/10.1177/14657503231179597 Verma, M. K., & Yuvaraj, M. (2023). (PDF) AI-Based Literature Reviews: A Topic Modeling Approach.https://www.researchgate.net/publication/370784698_AI Based_Literature_Reviews_A_Topic_Modeling_Approach Yu, D., & Xiang, B. (2023). Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling. Expert Systems with Applications, 225, 120114.