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Chapitre D'ouvrage Année : 2011

Sentiment analysis using automatically labelled financial news

Résumé

Given a corpus of financial news items labelled according to the market reaction following their publication, we investigate 'cotemporeneous' and forward looking price stock movements. Our approach is to provide a pool of relevant textual features to a machine learning algorithm to detect substantial stock price variations. Our two working hypotheses are that the market reaction to a news item is a good indicator for labelling financial news items, and that a machine learning algorithm can be trained on those news items to build models detecting price movement effectively.
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Dates et versions

hal-00783287 , version 1 (31-01-2013)

Identifiants

  • HAL Id : hal-00783287 , version 1

Citer

Michel Généreux, Thierry Poibeau, Moshe Koppel. Sentiment analysis using automatically labelled financial news. Khurshid Ahmad. Affective Computing and Sentiment Analysis: Metaphor, Ontology, Affect and Terminology, Springer, pp.111-126, 2011, Text, Speech and Language Technology, Vol. 45, 978-3-642-31863-4. ⟨hal-00783287⟩
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