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. © 2011 Springer-Verlag.
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Chaffar, S., & Inkpen, D. (2011). Using a heterogeneous dataset for emotion analysis in text. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6657 LNAI, pp. 62–67). Springer Verlag. https://doi.org/10.1007/978-3-642-21043-3_8
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