EmotionX-JTML: Detecting emotions with Attention
Johnny Torres · 2018
This paper addresses the problem of automatic recognition of emotions in text-only conversational datasets for the EmotionX challenge.Emotion is a human characteristic expressed through several modalities (e.g., auditory, visual, tactile), therefore, trying to detect emotions only from the text becomes a difficult task even for humans.This paper evaluates several neural architectures based on Attention Models, which allow extracting relevant parts of the context within a conversation to identify the emotion associated with each utterance.Empirical results the effectiveness of the attention model for the Emo-tionPush dataset compared to the baseline models, and other cases show better results with simpler models.