Detection of stance and sentiment modifiers in political blogs

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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 bi-grams 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.


External organisations
  • Linnaeus University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics


  • stance modifiers, sentiment modifiers, active learning, unsupervised features, resource-aware natural language processing
Original languageEnglish
Title of host publicationSpeech and computer
Subtitle of host publication19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings
EditorsAlexey Karpov, Rodmonga Potapova, Iosif Mporas
PublisherSpringer International Publishing
ISBN (Electronic)978-3-319-66429-3
ISBN (Print)978-3-319-66428-6
Publication statusPublished - 2017
Publication categoryResearch

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSpringer International Publishing

Related projects

Carita Paradis, Andreas Kerren, Magnus Sahlgren, Kostiantyn Kucher, Maria Skeppstedt & Vasiliki Simaki

Swedish Research Council


Project: Research

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