TL;DR
This paper introduces NewsTSC, a new dataset for target-dependent sentiment classification in news articles, revealing that news sentiment is less explicit and more context-dependent, with BERT improving classification performance.
Contribution
The paper presents NewsTSC, a manually annotated dataset for news sentiment analysis, and demonstrates BERT's effectiveness in capturing less explicit sentiment in news articles.
Findings
News sentiment in articles is less explicit and more context-dependent.
State-of-the-art TSC models perform worse on news data than on reviews or social media.
BERT significantly improves sentiment classification accuracy in news articles.
Abstract
Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the state of the art in TSC performs worse on news articles than on other…
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