Felix Hamborg


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NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
Felix Hamborg | Karsten Donnay
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly. In this paper, we investigate TSC in news articles, a much less researched TSC domain despite the importance of news as an essential information source in individual and societal decision making. We introduce NewsMTSC, a high-quality dataset for TSC on news articles with key differences compared to established TSC datasets, including, for example, different means to express sentiment, longer texts, and a second test-set to measure the influence of multi-target sentences. We also propose a model that uses a BiGRU to interact with multiple embeddings, e.g., from a language model and external knowledge sources. The proposed model improves the performance of the prior state-of-the-art from F1_m=81.7 to 83.1 (real-world sentiment distribution) and from F1_m=81.2 to 82.5 (multi-target sentences).


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Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling
Felix Hamborg
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Media bias can strongly impact the public perception of topics reported in the news. A difficult to detect, yet powerful form of slanted news coverage is called bias by word choice and labeling (WCL). WCL bias can occur, for example, when journalists refer to the same semantic concept by using different terms that frame the concept differently and consequently may lead to different assessments by readers, such as the terms “freedom fighters” and “terrorists,” or “gun rights” and “gun control.” In this research project, I aim to devise methods that identify instances of WCL bias and estimate the frames they induce, e.g., not only is “terrorists” of negative polarity but also ascribes to aggression and fear. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from the social sciences, where researchers have studied media bias for decades. The first results indicate the effectiveness of this interdisciplinary research approach. My vision is to devise a system that helps news readers to become aware of the differences in media coverage caused by bias.


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Self Organizing Maps for the Visual Analysis of Pitch Contours
Dominik Sacha | Yuki Asano | Christian Rohrdantz | Felix Hamborg | Daniel Keim | Bettina Braun | Miriam Butt
Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)