Exploring implicit sentiment evoked by fine-grained news events

Publication type
C1
Publication status
Published
Authors
Van Hee, C., De Clercq, O., & Hoste, V.
Series
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (EACL 2021)
Pagination
138-148
Publisher
Association for Computational Linguistics
Conference
Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA), held in conjunction with EACL 2021 (Online)
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Abstract

We investigate the feasibility of defining sentiment evoked by fine-grained news events. Our research question is based on the premise that methods for detecting implicit sentiment in news can be a key driver of content diversity, which is one way to mitigate the detrimental effects of filter bubbles that recommenders based on collaborative filtering may produce. Our experiments are based on 1,735 news articles from major Flemish newspapers that were manually annotated, with high agreement, for implicit sentiment. While lexical resources prove insufficient for sentiment analysis in this data genre, our results demonstrate that machine learning models based on SVM and BERT are able to automatically infer the implicit sentiment evoked by news events.