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Emotion computing using Word Mover’s Distance features based on Ren_CECps

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  • Fuji Ren
  • Ning Liu

Abstract

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover’s Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

Suggested Citation

  • Fuji Ren & Ning Liu, 2018. "Emotion computing using Word Mover’s Distance features based on Ren_CECps," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-17, April.
    • Handle: RePEc:plo:pone00:0194136
    DOI: 10.1371/journal.pone.0194136
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