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2017 | OriginalPaper | Buchkapitel

Detection of Stance and Sentiment Modifiers in Political Blogs

verfasst von : Maria Skeppstedt, Vasiliki Simaki, Carita Paradis, Andreas Kerren

Erschienen in: Speech and Computer

Verlag: Springer International Publishing

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Abstract

The automatic detection of seven types of modifiers was studied: Certainty, Uncertainty, Hypotheticality, Prediction, Recommendation, Concession/Contrast and Source. A classifier aimed at detecting local cue words that signal the categories was the most successful method for five of the categories. For Prediction and Hypotheticality, however, better results were obtained with a classifier trained on tokens and bigrams present in the entire sentence. Unsupervised cluster features were shown useful for the categories Source and Uncertainty, when a subset of the training data available was used. However, when all of the 2,095 sentences that had been actively selected and manually annotated were used as training data, the cluster features had a very limited effect. Some of the classification errors made by the models would be possible to avoid by extending the training data set, while other features and feature representations, as well as the incorporation of pragmatic knowledge, would be required for other error types.

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Fußnoten
1
We are very grateful to the Swedish Research Council (framework grant “the Digitized Society – Past, Present, and Future” with No. 2012-5659), to Tom Sköld for data annotation and to Kostiantyn Kucher for annotation tool construction.
 
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Metadaten
Titel
Detection of Stance and Sentiment Modifiers in Political Blogs
verfasst von
Maria Skeppstedt
Vasiliki Simaki
Carita Paradis
Andreas Kerren
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-66429-3_29