Abstract
Unwholesome lifestyles can reduce lifespan by several years or even decades. Therefore, raising awareness and promoting healthier behaviors prove essential to revert this dramatic panorama. Virtual coaching systems are at the forefront of digital solutions to educate people and procure a more effective health self-management. Despite their increasing popularity, virtual coaching systems are still regarded as entertainment applications with an arguable efficacy for changing behaviors, since messages can be perceived to be boring, unpersonalized and can become repetitive over time. In fact, messages tend to be quite general, repetitive and rarely tailored to the specific needs, preferences and conditions of each user. In the light of these limitations, this work aims at help building a new generation of methods for automatically generating user-tailored motivational messages. While the creation of messages is addressed in a previous work, in this paper the authors rather present a method to automatically extract the semantics of motivational messages and to create the ontological representation of these messages. The method uses first natural language processing to perform a linguistic analysis of the message. The extracted information is then mapped to the concepts of the motivational messages ontology. The proposed method could boost the quantity and diversity of messages by automatically mining and parsing existing messages from the internet or other digitised sources, which can be later tailored according to the specific needs and particularities of each user.
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Acknowledgments
This work was supported by Project TIN2015-71873-R (Spanish Ministry of Economy and Competitiveness -MINECO- and the European Regional Development Fund -ERDF).
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Villalonga, C. et al. (2017). Automatic Mapping of Motivational Text Messages into Ontological Entities for Smart Coaching Applications. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham. https://doi.org/10.1007/978-3-319-67585-5_36
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