Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation

Understand

Emotion detection in text is an important task in NLP and is essential in many applications.

• Most of the existing methods treat this task as a problem of single-label multi-class text classification.
• To predict multiple emotions for one instance, most of the existing works regard it as a general Multi-label Classification (MLC) problem, where they usually either apply a manually determined threshold on the last output layer of their neural network models or train multiple binary classifiers and make predictions in the fashion of one-vs-all.
• However, compared to labels in the general MLC datasets, the number of emotion categories are much fewer (less than 10).