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.