Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Table S3: Results on single label emotion classification
['Models', 'Macro P.', 'ISEAR Macro R.', 'Macro F1', 'Micro F1']
In our experiments, Seq2Emo is outperformed by binary-SLD model on the CBET dataset. In this section, we show that SLD is a strong baseline that is able to achieve great performance on single label emotion classification task. Compared to binary-SL and binary-SLD, only one single FC layer with Softmax regularizer is used to map the output of the encoders to the space of labels. In the state-of-the-art emotion classification work by [ZhangFSZWY18], they use lexicons to estimate the emotion distribution and use multi-task learning to train a CNN text classifier (MTCNN). We run three single labeled emotional datasets against their work: ISEAR [scherer1994evidence], TEC [mohammad2012emotional] and only single labeled instances in CBET dataset. We compare MTCNN with SL, SLD models and three other non- deep learning models (Naïve Bayes, Random Forest, and SVM) which use bag-of-word features to represent words. It needs to be mentioned that in the work of [ZhangFSZWY18], the performance of MTCNN is measured using the averages scores of the evaluation set of the 10-fold cross validation, whereas we run the numbers on a held-out test (10% of the original dataset). ISEAR has only 7,666 instances in total, whereas TEC has 21,051 instances and CBET-Single 76,860 instances. The fact that MTCNN has better scores on the ISEAR maybe because the dataset is too small to be generalized on the held-out test set.