User and Context-centric Content Analysis

This WP implements models for representing contextual, sentiment and online social interaction features, as well as deploys linguistic processing at different levels of accuracy and completeness. Our approach is based on disambiguated entities, relations between them, subjective expressions, opinion holders and, relations between pieces of sentiment-rich information. For more details, please refer to [1,2].

[1] Ioannis Arapakis, Filipa Peleja, B. Barla Cambazoglu, Joao Magalhaes. Linguistic Benchmarks of Online News Article Quality. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin, Germany, 2016.

[2] Y. Mehmood, F. Bonchi, D. Garcia-Soriano Spheres of Influence for More Effective Viral Marketing In Proceedings of the 2016 ACM SIGMOD Conference (SIGMOD 2016) :copyright:ACM June 26 - July 1, 2016, San Francisco, CA, USA.