  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 domains (average
recall AvgRec = 69.8 on NewsTSC compared to AvgRev = [75.6, 82.2] on
established TSC datasets). Reasons include incorrectly resolved relation of
target and sentiment-bearing phrases and off-context dependence. As a major
improvement over previous news TSC, we find that BERT's natural language
understanding capabilities capture the less explicit sentiment used in news
articles.
