Matina Halkia


Conflict Event Modelling: Research Experiment and Event Data Limitations
Matina Halkia | Stefano Ferri | Michail Papazoglou | Marie-Sophie Van Damme | Dimitrios Thomakos
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

This paper presents the conflict event modelling experiment, conducted at the Joint Research Centre of the European Commission, particularly focusing on the limitations of the input data. This model is under evaluation as to potentially complement the Global Conflict Risk Index (GCRI), a conflict risk model supporting the design of European Union’s conflict prevention strategies. The model aims at estimating the occurrence of material conflict events, under the assumption that an increase in material conflict events goes along with a decrease in material and verbal cooperation. It adopts a Long-Short Term Memory Cell Recurrent Neural Network on country-level actor-based event datasets that indicate potential triggers to violent conflict such as demonstrations, strikes, or elections-related violence. The observed data and the outcome of the model predictions consecutively, consolidate an early warning alarm system that signals abnormal social unrest upheavals, and appears promising as an approach towards a conflict trigger model. However, event-based systems still require overcoming certain obstacles related to the quality of the input data and the event classification method.


Sentiment Analysis in the News
Alexandra Balahur | Ralf Steinberger | Mijail Kabadjov | Vanni Zavarella | Erik van der Goot | Matina Halkia | Bruno Pouliquen | Jenya Belyaeva
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which we apply these concepts. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.