Rezvan MohammadiBaghmolaei

Word embedding is the process of converting words into vectors of real numbers which is of great interest in natural language processing. Recently, the performance of word embedding models has been the subject of some studies in emotion analysis. They mainly try to embed affective aspects of words into their vector representations utilizing some external sentiment/emotion lexica. The underlying emotion models in the existing studies follow basic emotion theories in psychology such as Plutchik or VAD. However, none of them investigate the Mixed Emotions (ME) model in their work which is the most precise theory of emotions raised in the recent psychological studies. According to ME, feelings can be the consequent of multiple emotion categories at the same time with different intensities. Relying on the ME model, this article embeds mixed emotions features into the existing word-vectors and performs extensive experiments on various English datasets. The analyses in both lines of intrinsic evaluations and extrinsic evaluations prove the improvement of the presented model over the existing emotion-aware embeddings such as SAWE and EWE.