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Detection of stance and sentiment modifiers in political blogs

Skeppstedt, Maria; Simaki, Vasiliki LU ; Paradis, Carita LU and Kerren, Andreas (2017) In Lecture Notes in Artificial Intelligence 10458. p.302-311
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 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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
stance modifiers, sentiment modifiers, active learning, unsupervised features, resource-aware natural language processing
in
Lecture Notes in Artificial Intelligence
editor
Karpov, Alexey; Potapova, Rodmonga ; Mporas, Iosif ; ; and
volume
10458
pages
302 - 311
publisher
Springer International Publishing
ISBN
978-3-319-66428-6
language
English
LU publication?
yes
id
b171ca83-1af6-43b1-8563-5f88b82fed02
date added to LUP
2017-06-02 19:44:08
date last changed
2017-08-30 16:05:09
@inbook{b171ca83-1af6-43b1-8563-5f88b82fed02,
  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 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. },
  author       = {Skeppstedt, Maria and Simaki, Vasiliki and Paradis, Carita and Kerren, Andreas},
  editor       = {Karpov, Alexey and Potapova, Rodmonga  and Mporas, Iosif },
  isbn         = {978-3-319-66428-6},
  keyword      = {stance modifiers, sentiment modifiers, active learning, unsupervised features, resource-aware natural language processing},
  language     = {eng},
  pages        = {302--311},
  publisher    = {Springer International Publishing},
  series       = {Lecture Notes in Artificial Intelligence},
  title        = {Detection of stance and sentiment modifiers in political blogs},
  volume       = {10458},
  year         = {2017},
}