Abstract
For a long time, both professionals and the lay public showed little interest in informal carers. Yet these people deals with multiple and common issues in their everyday lives. As the population is aging we can observe a change of this attitude. And thanks to the advances in computer science, we can offer them some effective assistance and support by providing necessary information and connecting them with both professional and lay public community. In this work we describe a project called “Research and development of support networks and information systems for informal carers for persons after stroke” producing an information system visible to public as a web portal. It does not provide just simple a set of information but using means of artificial intelligence, text document classification and crowdsourcing further improving its accuracy, it also provides means of effective visualization and navigation over the content made by most by the community itself and personalized on a level of informal carer’s phase of the care-taking timeline. In can be beneficial for informal carers as it allows to find a content specific to their current situation. This work describes our approach to classification of text documents and its improvement through crowdsourcing. Its goal is to test text documents classifier based on documents similarity measured by N-grams method and to design evaluation and crowdsourcing-based classification improvement mechanism. Interface for crowdsourcing was created using CMS WordPress. In addition to data collection, the purpose of interface is to evaluate classification accuracy, which leads to extension of classifier test data set, thus the classification is more successful.
Highlights
Informal carers deal with many difficult situations when care-taking their relatives
We briefly describe our experience and summarize our previous results of Czech, Slovak and English text documents classification which serve as a base for this work [42]
In [21] authors compared various kinds of low-level features, including those extracted through deep learning and conclude that keywords suggested by the crowd, established through a crowd- sourcing platform can be effectively used for training sentiment classification models for short texts and that those models are at least as effective as the ones that are developed through deep learning or even better [21]
Summary
Informal carers deal with many difficult situations when care-taking their relatives. We created a web portal providing all necessary information which should help them to improve their care-taking, but it requires a meaningful and effective navigation over the content made from the most by carers themselves. We provide visual mean attempting to place the carer to a correct position on a caretaking timeline. It is all implemented in a web server running WordPress CMS. In our effort we utilize so-called natural language processing (NLP), which is usually used in for example information extraction tasks or text classification, where it helps to automate and speed up the classification process. Intent of the project is to create and validate the pilot IS IC model in Moravian-Silesian Region by 12/2021, which can subsequently be applied in other regions and / or other target IC groups
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