Using a heterogeneous dataset for emotion analysis in text

Authors:
Soumaya Chaffar;Diana Inkpen
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada
Venue:
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Year:
2011

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Abstract

In this paper, we adopt a supervised machine learning approach to recognize six basic emotions (anger, disgust, fear, happiness, sadness and surprise) using a heterogeneous emotion-annotated dataset which combines news headlines, fairy tales and blogs. For this purpose, different features sets, such as bags of words, and N-grams, were used. The Support Vector Machines classifier (SVM) performed significantly better than other classifiers, and it generalized well on unseen examples.