Title: Survey on-demand: a versatile scientific article automated inquiry method using text mining applied to asset liability management
Authors: Igor Ferreira Do Nascimento; Pedro Henrique De Melo Albuquerque; Yaohao Peng
Addresses: University of Brasília, Campus Darcy Ribeiro, Brasília, Distrito Federal, Brazil; Federal Institute of Education, Science and Technology of Piauí, Teresina, Piauí, Brazil ' University of Brasília, Campus Darcy Ribeiro, Brasília, Distrito Federal, Brazil ' University of Brasília, Campus Darcy Ribeiro, Brasília, Distrito Federal, Brazil
Abstract: We proposed a methodology that automatically relates content of text documents with lexical items. The model estimates whether an article addresses a specific research object based on the relevant words in its abstract and title using text mining and partial least square discriminant analysis. The model is efficient in accuracy and the adjustment and validation indicators are either superior or equal to the other models in the literature on text classification. In comparison to existing methods, our method offers highly interpretable outcomes and allows flexible measurements of word frequency. The proposed solution may aid scholars regarding the process of searching theoretical references and suggesting scientific articles based on the similarities among the used vocabulary. Applied to the finance area, our framework has indicated that approximately 10% of the publications in the selected journals that address the subject of asset liability management. Moreover, we highlight the journals with the largest number of publications over time and the keywords about the subject using only freely accessible information.
Keywords: text classification; text mining; bibliometric; dimensionality reduction; asset liability management.
International Journal of Business Intelligence and Data Mining, 2021 Vol.18 No.3, pp.261 - 290
Received: 09 Mar 2018
Accepted: 30 Jun 2018
Published online: 26 Feb 2021 *