Digital transformation is changing people's lives in many ways, creating competition between people and machines. All aspects of people’s lives are being influenced with global impacts for society. In this context, many problems have emerged for which there is still no clear ideas of their effects on people's lives. To study these problems, new tools and methodologies are needed in order to compare large volumes of data. The analysis of texts, using Text Mining, has been gaining prominence, among researchers, as one of the most relevant methodologies. However, methodologies using Text Mining are not robust enough to allow researchers to compare data from different sources, such as report data and text data. The main objective of this paper is to propose an innovative Text Mining methodology that allows to compare different texts. This study is exploratory, and it is supported by quantitative methodologies. Using Text Mining to explore ECIAIR 2019 proceedings and other European reputed reports about digital transformation, and comparing the opinions expressed by researchers with those manifested by other people, it is intended to understand if there are coincidences in the language used by researchers and on the reports in what concerns what people feel about the impacts of digital transformation on their lives. This paper belongs to an ongoing research aiming to develop text mining tools that consider corpora as variables with specific values, treating those variables as statistic variables, contributing to the enrichment of the statistical methodologies used to study digital transformation impacts. The results show that there is a gap between the language of the investigators and the one used on the reports. At the same time, there are also overlaps in some topics analysed in the documents. These results indicate that there are topics that concern both the scientific community and the international organisations responsible for the preparation of public policy guiding reports.

--

Cluster analysis,Digital transformation,People,Robots,Text mining