@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",
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://aclanthology.org/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).",
}
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%0 Conference Proceedings
%T NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles
%A Hamborg, Felix
%A Donnay, Karsten
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F hamborg-donnay-2021-newsmtsc
%X 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).
%R 10.18653/v1/2021.eacl-main.142
%U https://aclanthology.org/2021.eacl-main.142
%U https://doi.org/10.18653/v1/2021.eacl-main.142
%P 1663-1675
Markdown (Informal)
[NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles](https://aclanthology.org/2021.eacl-main.142) (Hamborg & Donnay, EACL 2021)
ACL