A Data Enhancement Method Based on Mongolian Emotion Distribution Learning
The existing emotion distribution learning has not been applied to Mongolian,and there is no research on the use of emotion distribution learning for data enhancement.Based on this,this study integrates the idea of polar coordinates into the Plutchik's wheel of emotions,proposes a polar coordinates emotion representation method,and transforms the emotion distribution into compound emotion vector and integrates the attention information of the emo-tion wheel into the model for Mongolian emotion distribution learning.By using the feature that any two basic emotions in Plutchik's wheel of emotions can be mixed to form binary emotions,we expand more rich emotion labels for the pre-dicting composite emotion vector,so as to achieve the purpose of expanding the dataset.Comparative experiments on Mongolian,Chinese and English datasets show that the performance of emotion distribution learning based on polar co-ordinate emotion representation is better than that of traditional methods.
Emotion distribution learningMongolianData enhancementPolar coordinatesPlutchik's wheel of e-motionsBinary emotion
NETL
NSTL
万方数据
