Publication: NewsMTSC: a dataset for (multi-)target-dependent sentiment classification in political news articles
NewsMTSC: a dataset for (multi-)target-dependent sentiment classification in political news articles
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Hamborg, F., & Donnay, K. (2021). NewsMTSC: a dataset for (multi-)target-dependent sentiment classification in political news articles. In P. Merlo & A. Association for Computational Linguistics (Eds.), Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1663–1675). Association for Computational Linguistics (ACL).
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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, differ
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Hamborg, F., & Donnay, K. (2021). NewsMTSC: a dataset for (multi-)target-dependent sentiment classification in political news articles. In P. Merlo & A. Association for Computational Linguistics (Eds.), Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1663–1675). Association for Computational Linguistics (ACL).
