Corpus of News Articles Annotated with Article Level Sentiment

Ahmet Aker, Marius Hamacher, Alicia Nti, Anne Smets, Hauke Gravenkamp, Johannes Erdmann, Sabrina Mayer, Julia Serong, Anna Welpinghus, Francesco Marchi
2019 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval  
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
more » ... 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.
dblp:conf/sigir/AkerHNSGEMSWM19 fatcat:o7jvsnfv7jfmridkl3vi7pj7c4