Title | ||
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NewsMTSC - A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles. |
Abstract | ||
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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, 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). |
Year | Venue | DocType |
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2021 | EACL | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
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felix hamborg | 1 | 19 | 9.34 |
Karsten Donnay | 2 | 0 | 0.34 |