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Publications (9)
We present¹ a personalized ingredient-based Deep Learning recommender on the food domain that exploits ingredients and nutrition information to create recipe representations and propose to every user a more personalized and healthier meal. The recommender will be a critical component in our Meal Prediction Tool (MPT) designed with a focus on the pe...
We present a data-driven linguistic approach for exploring the predominant targets of xenophobia-motivated behavior in Greece over time focusing on specific Target Groups of interest. We employ two principal data analytics workflows to produce the corresponding data insights; Event Analysis using news data from 7 different sources for the last two...
The basic aim of our research effort is to examine the phenomenon of xenophobia in Greece through a large-scale multi-source study based on the use of advanced computational social science approaches. There is a common perception that xenophobia is a deep-rooted social phenomenon that reasonably escalates under circumstances of severe economic cris...
Citizens are shaping their food preferences and expressing their food experiences on a daily basis reflecting their way of living, culture and well-being . In this paper, we focus on food perceptions and experiences in the context of smart citizen and tourist sensing. We analyze Foursquare user reviews about food-related points of interest in ten E...
Presents the research about Xenophobia in Greece.
Evaluations and recommendations expressed in text towards particular targets as specific subareas of the general positive/negative sentiment landscape.
In this paper we present a method for the automatic detection of user-stated intentions in terms of desires, purposes and commitments as specific insights deriving from the semantics of the intention expressions. The method is based on a linguistic data-driven and domain-independent framework for textual intention analysis and achieves substantial...
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Projects (3)
CAPSELLA develops innovative ICT solutions tailored to the needs of all food, field and seed related actors engaging in agrobiodiversity, harnessing the power of open data to foster community-led innovation.