NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
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).- Anthology ID:
- 2021.eacl-main.142
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1663–1675
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.142
- DOI:
- 10.18653/v1/2021.eacl-main.142
- Cite (ACL):
- Felix Hamborg and Karsten Donnay. 2021. NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1663–1675, Online. Association for Computational Linguistics.
- Cite (Informal):
- NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles (Hamborg & Donnay, EACL 2021)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2021.eacl-main.142.pdf
- Code
- fhamborg/newsmtsc
- Data
- NewsMTSC