This paper describes our system participating in the SemEval 2016 task: Detecting stance in Tweets. The goal was to identify whether the author of a tweet is in favor of the given target or against. Our approach is based on a maximum entropy classifier, which uses surface-level, sentiment and domain-specific features. We participated in both the supervised and weakly supervised subtasks and received promising results for most of the targets.

Sentiment analysis is a wide area with great potential and many research directions. One direction is stance detection, which is somewhat similar to sentiment analysis. We supplement stance detection dataset with sentiment annotation and explore the similarities of these tasks. We show that stance detection and sentiment analysis can be mutually beneficial by using gold label for one task as features for the other task. We analysed the presence of target entities for stance detection in the dataset. We outperform the state-of-the-art results for stance detection in Czech and set new state-of-the-art results for the newly created sentiment analysis part of the extended dataset.

We introduce Flames Detector, an online system for measuring flames, i.e. strong negative feelings or emotions, insults or other verbal offences, in news commentaries across five languages. It is designed to assist journalists, public institutions or discussion moderators to detect news topics which evoke flames. We propose a machine learning approach to flames detection and calculate an aggregated score for a set of comment threads. The demo application shows the most flaming topics of the current period in several language variants. The search functionality gives a possibility to measure flames in any topic specified by a query. The evaluation shows that the flame detection in discussions is a difficult task, however, the application can already reveal interesting information about the actual news discussions.

We propose a novel metric for evaluating summary content coverage. The evaluation framework follows the Pyramid approach to measure how many summarization content units, considered important by human annotators, are contained in an automatic summary. Our approach automatizes the evaluation process, which does not need any manual intervention on the evaluated summary side. Our approach compares abstract meaning representations of each content unit mention and each summary sentence. We found that the proposed metric complements well the widely-used ROUGE metrics.