Detection of Stance and Sentiment Modifiers in Political Blogs

  • Maria Skeppstedt
  • Vasiliki Simaki
  • Carita Paradis
  • Andreas Kerren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10458)


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.


Stance modifiers Sentiment modifiers Active learning Unsupervised features Sesource-aware natural language processing 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maria Skeppstedt
    • 1
  • Vasiliki Simaki
    • 1
    • 2
  • Carita Paradis
    • 2
  • Andreas Kerren
    • 1
  1. 1.Department of Computer ScienceLinnaeus UniversityVäxjöSweden
  2. 2.Centre for Languages and LiteratureLund UniversityLundSweden

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