Cross-lingual Flames Detection in News Discussions

Josef Steinberger and Tomáš Brychcín and Tomáš Hercig and Peter Krejzl
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017 (2017)
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Abstract

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 wrangles. 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.

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