Research on sentiment analysis is in its mature status. Studies on this topic have proposed various solutions and datasets to guide machine-learning approaches. However, so far the sentiment scoring is restricted to the level of short textual units such as sentences. Our comparison shows that there is a huge gap between machines and human judges when the task is to determine sentiment scores of a longer text such as a news article. To close this gap, we propose a new human-annotated dataset

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... aining 250 news articles with sentiment labels at article level. Each article is annotated by at least 10 people. The articles are evenly divided into fake and non-fake categories. Our investigation on this corpus shows that fake articles are significantly more sentimental than non-fake ones. The dataset will be made publicly available.