| Autor: |
Aker, A., Hauke Gravenkamp, Mayer, S. J., Hamacher, M., Smets, A., Nti, A., Erdmann, J., Serong, J., Welpinghus, A., Marchi, F. |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Scopus-Elsevier |
| Popis: |
OA platinum Research on sentiment analysis is in its ma- ture status. Studies on this topic have pro- posed 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 containing 250 news articles with sen- timent labels at article level. Each article is annotated by at least 10 people. The articles are evenly divided into fake and non-fake cate- gories. Our investigation on this corpus shows that fake articles are significantly more senti- mental than non-fake ones. The dataset will be made publicly available. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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