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

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.

Copyright: © 2018 Ren, Liu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.