Abstract
Abstract. Research on knowledge discovery in the geospatial domain currently focuses on semi-structured, even on unstructured rather than fully structured content. The attention has been put on the plethora of resources on the Web, such as html pages, news articles, blogs, social media etc. Semantic information extraction in geospatial-oriented approaches is further used for semantic analysis, search, and retrieval. The aim of this paper is to extract, analyse and visualize geospatial semantic information and emotions from texts on climate change. A collection of articles on climate change is used to demonstrate the developed approach. These articles describe environmental and socio-economic dimensions of climate change across the Earth, and include a wealth of information related to environmental concepts and geographic locations affected by it. The results are analysed in order to understand which specific human emotions are associated with environmental concepts and/or locations, as well as which environmental terms are linked to locations. For the better understanding of the above-mentioned information, semantic networks are used as a powerful visualization tool of the links among concepts – locations – emotions.
Highlights
Nowadays, data and information are considered valuable "assets", as humans have realised their importance in terms of solving complex problems and facilitating the decision-making process
The wealth of unstructured data presents a major challenge and an opportunity for the geospatial domain, since these data contain valuable information in terms of geospatial concepts, locations, events, phenomena, and activities occurring in space
Environmental terms are extracted using an ontologybased information extraction approach based on General Multilingual Environmental Thesaurus (GEMET), the GEneral Multilingual Environmental Thesaurus, (GEMET, 2021)
Summary
Data and information are considered valuable "assets", as humans have realised their importance in terms of solving complex problems and facilitating the decision-making process. Thereby, tools and techniques have been developed, in order to extract valuable information from unstructured data. The wealth of unstructured data presents a major challenge and an opportunity for the geospatial domain, since these data contain valuable information in terms of geospatial concepts, locations, events, phenomena, and activities occurring in space. There is a need to explore and develop methods in order to extract such information from unstructured data and link it to geographical locations. This would enable the search of such data using spatial criteria, as well as, a better understanding of complex, interconnected, and interacting environmental and socio-economic challenges. Going a step further, analysing sentiments related to environmental phenomena and problems could help us gain a better comprehension of people’s concerns and opinions on the greatest risks of the planet such as climate change
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