Negation scope detection in sentiment analysis: Decision support for news-driven trading

By Nicolas Pröllochs, Stefan Feuerriegel, Dirk Neumann. 2016

55 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mark Dras
2. Leonardo dos Santos Pinheiro
3. Helmut Prendinger
4. Stefan Feuerriegel

In Management Science and Operations Research

1. Mark Dras
2. Leonardo dos Santos Pinheiro
3. Helmut Prendinger
4. Stefan Feuerriegel
5. Longbing Cao
6. Dogu Araci

Citations in the corpus

• News-based trading strategies (2016, background): “This demonstrates how the narrative content of disclosures [5, 6, 7, 8, 9, 10] can be harnessed to provide decision support for investors.”
• Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, methodology): “As with all sentiment analysis, scope of negation can be an issue: Pr¨ollochs et al. (2016) recently proposed a reinforcement learning method to predict negation scope and showed that it improved the accuracy on a dataset from the financial news domain.”