Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Table 3: Comparison between different word-level emotion representations with state-of-the-art results. The best results are in bold. New state-of-the-art results Emo2Vec that achieves are highlighted with boxes.
['dataset SS-Twitter', 'Previous SOTA results bi-LSTM\xa0Felbo et\xa0al. ( 2017 )', 'Previous SOTA results 0.88', 'GloVe 0.78', 'GloVe+DeepMoji  [BOLD] 0.81', 'GloVe+Emo2Vec  [BOLD] 0.81']
Compared with GloVe+DeepMoji, GloVe+Emo2Vec achieves same or better results on 11/14 datasets, which on average gives 1.0% improvement. GloVe+Emo2Vec achieves better performances on SOTA results on three datasets (SE0714, stress and tube_tablet) and comparable result to SOTA on another four datasets (tube_auto, SemEval, SCv1-GEN and SCv2-GEN). We believe the reason why we achieve a much better performance than SOTA on the SE0714 is that headlines are usually short and emotional words exist more commonly in headlines. Thus, to detect the corresponding emotion, more attention needs to be paid to words.