Mining the Ambiguity: Correspondence and network analysis for discovering word sense

Assuming that language can be modelled as a network of words, it is difficult to mine knowledge in textual data bases, due to their high dimensionality and the ambiguity which characterises words and their use. From a methodological viewpoint, here we propose a strategy for stressing the differences in the manifest relations emerging by Network Analysis (NA) and the latent relations obtained by lexical Correspondence Analysis (CA). Aim of this paper is to deal with the word-sense disambiguation problem, not in the usual pre-processing step, but during the analysis. The results applied to the analysis of a management commentary are presented in order to propose some statistical lexical sources, useful in the peculiar domain of business information

Text Mining tools for extracting knowledge from firms annual reports

This paper has been developed in the frame of the European project BLUE-ETS (Economic and Trade Statistics), in the work-package devoted to propose new tools for collecting and analysing data. In order to obtain business information by documentary repositories, here we refer to documents produced with non statistical aims. The use of secondary sources, typical of data and text mining, is an opportunity not sufficiently explored by National Statistical Institutes. NSIs aim at collecting and representing information in a usable and easy-readable way. The use of textual data has been still viewed as too problematic, because of the complexity and the expensiveness of the pre-processing procedures and often for the lack of suitable analytical tools. Our aim is to identify statistical linguistic sources by a deep analysis of one management commentary. From a methodological viewpoint, here we propose a tool for exploring relations between words at a micro-data level, derived from network data analysis, namely ego networks, applied together with lexical correspondence analysis

Tourist and Viral Mobilities Intertwined: Clustering COVID-19-Driven Travel Behaviour of Rural Tourists in South Tyrol, Italy

Travel patterns have dramatically changed during the COVID-19 pandemic. Tourism has been both a vector and a victim of the disease. This paper explores the pandemic’s impact on rural tourism, using the theoretical framework of the “mobilities turn” to investigate issues of corporeal and communicative travel found between the first and second waves of the COVID-19 pandemic. A sample of 874 guests visiting the Italian region of South Tyrol, where rural tourism is the norm, identified different patterns of physical travel and approaches to collecting on-site information on COVID-19. Results from a principal component analysis (PCA) and a cluster analysis highlighted at least two different approaches from visitors to the region: the first is more cautious, mostly practiced by domestic tourists, with limited mobility on-site, coupled with a need for information; the second is instead a more adventurous approach, with higher on-site mobility, more use of sustainable forms of transport and less interest in data evidence on COVID-19. Implications for rural tourism and its future are discussed. The hypothesis of an inverse relationship between corporeal and communicative travel needs further exploration in future research