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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...