Detecting Negation Scopes for Financial News Sentiment Using Reinforcement Learning

Applying natural language processing to the domain of financial news requires robust methods that process all sentences correctly, including those that are negated. So far, related research commonly utilizes rule-based algorithms to detect negated sentence fragments, named negation scopes. Nonethele...

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Vydané v:2016 49th Hawaii International Conference on System Sciences (HICSS) s. 1164 - 1173
Hlavní autori: Prollochs, Nicolas, Feuerriegel, Stefan, Neumann, Dirk
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.01.2016
Predmet:
Conferences
Flexible structures
Hidden Markov models
Learning
Learning (artificial intelligence)
Manuals
Natural language processing
News
Pragmatics
Raw materials
Reinforcement
Sentences
Sentiment analysis
Stock markets
ISSN:1530-1605, 1530-1605
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Shrnutí:Applying natural language processing to the domain of financial news requires robust methods that process all sentences correctly, including those that are negated. So far, related research commonly utilizes rule-based algorithms to detect negated sentence fragments, named negation scopes. Nonetheless, these methods involve certain limitations when encountering complex language or particularities of the chosen prose. As an alternative, reinforcement learning offers an opportunity to learn suitable negation classifications through trial-and-error experience. This method tries to replicate human-like learning and thus appears well-suited for natural language processing. Its episode-based and flexible structure allows for the handling of even highly complex sentences. Our results provide evidence that reinforcement learning can outperform rule-based approaches from the related literature. The best performing implementation reveals a predictive accuracy of up to 76.37% on a manually-labeled dataset, exceeding the predictive accuracy of rule-based approaches by 2.55 %. When utilizing the already trained reinforcement learning implementation for sentiment analysis, we find a potential subjectivity bias that limits the predictive performance of forecasting stock market returns.
Bibliografia:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:1530-1605
1530-1605
DOI:10.1109/HICSS.2016.147