@inproceedings{Hamborg2021NewsM-53815,
  year={2021},
  title={NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles},
  url={https://www.aclweb.org/anthology/2021.eacl-main.142/},
  publisher={Association for Computational Linguistics},
  address={Stroudsburg, PA},
  booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  pages={1663--1675},
  editor={Merlo, Paola and Tiedemann, Jorg and Tsarfaty, Reut},
  author={Hamborg, Felix and Donnay, Karsten}
}

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    <dcterms:title>NewsMTSC : A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles</dcterms:title>
    <dcterms:issued>2021</dcterms:issued>
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    <dcterms:abstract xml:lang="eng">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).</dcterms:abstract>
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