EmoZen: A Robust Word Embedding for Implicit and Explicit Expressions of Emotion

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use.