The fourth chapter compares European project reports and illustrates the main comparative results extracted by a text-mining procedure used to explore the five national reports generated from each case study, by means of a sociocultural profiling method based on a natural language processing procedure. In European projects, there is often a phase in which the results are compared with qualitative, quantitative, or mixed methods. Among the various elements that organize the com- parison, an important component is the cultural dimension since it organizes social actors’ practices, often carried out with qualitative methods. However, in line with the literature, this dimension is detectable through text mining methods since it determines the choice and association of the words used to organize communica- tion. This chapter proposes a text-mining procedure for comparing the documents’ symbolic-cultural categories. In particular, Emotional Text Mining was used to study the cultural differences in digital development in higher education among countries by analyzing the country partners’ reports to identify the symbolic- cultural categories and the evolution of the EHEA during the translation process.
i Digital Development Culture in Europe
capogna
2024-01-01
Abstract
The fourth chapter compares European project reports and illustrates the main comparative results extracted by a text-mining procedure used to explore the five national reports generated from each case study, by means of a sociocultural profiling method based on a natural language processing procedure. In European projects, there is often a phase in which the results are compared with qualitative, quantitative, or mixed methods. Among the various elements that organize the com- parison, an important component is the cultural dimension since it organizes social actors’ practices, often carried out with qualitative methods. However, in line with the literature, this dimension is detectable through text mining methods since it determines the choice and association of the words used to organize communica- tion. This chapter proposes a text-mining procedure for comparing the documents’ symbolic-cultural categories. In particular, Emotional Text Mining was used to study the cultural differences in digital development in higher education among countries by analyzing the country partners’ reports to identify the symbolic- cultural categories and the evolution of the EHEA during the translation process.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.