Detection of stance and sentiment modifiers in political blogs

Skeppstedt, Maria and Simaki, Vasiliki and Paradis, Carita and Kerren, Andreas (2017) Detection of stance and sentiment modifiers in political blogs. In: SPECOM 2017. Lecture Notes in Computer Science . Springer, Cham, pp. 302-311. ISBN 9783319665286

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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.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-66429-3_29
Subjects:
ID Code:
124809
Deposited By:
Deposited On:
23 Apr 2018 13:58
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Sep 2020 06:24