Abstract
Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using Multi-Label K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.
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© 2009 Springer-Verlag Berlin Heidelberg
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Bhowmick, P.K., Basu, A., Mitra, P., Prasad, A. (2009). Multi-label Text Classification Approach for Sentence Level News Emotion Analysis. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_42
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DOI: https://doi.org/10.1007/978-3-642-11164-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11163-1
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