@inproceedings{hamborg-donnay-2021-newsmtsc,
    title = "{N}ews{MTSC}: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles",
    author = "Hamborg, Felix  and
      Donnay, Karsten",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.eacl-main.142/",
    doi = "10.18653/v1/2021.eacl-main.142",
    pages = "1663--1675",
    abstract = "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)."
}