Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data.

Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data.

Electronic Health Records at People's Hands Across Europe: The InteropEHRate Protocols.

Sharing of personal health data could facilitate and enhance the quality of care and the conduction of further research studies. However, these data are still underutilized due to legal, technical, and interoperability challenges, whereas the data subjects are not able to manage, interact, and decide on what to share, with whom, and for what purposes. This barrier obstacles continuity of care across in the European Union (EU), and neither healthcare providers nor data researchers nor the citizens are benefiting through efficient healthcare treatment and research.