  Word embedding models such as GloVe are widely used in natural language
processing (NLP) research to convert words into vectors. Here, we provide a
preliminary guide to probe latent emotions in text through GloVe word vectors.
First, we trained a neural network model to predict continuous emotion valence
ratings by taking linguistic inputs from Stanford Emotional Narratives Dataset
(SEND). After interpreting the weights in the model, we found that only a few
dimensions of the word vectors contributed to expressing emotions in text, and
words were clustered on the basis of their emotional polarities. Furthermore,
we performed a linear transformation that projected high dimensional embedded
vectors into an emotion space. Based on NRC Emotion Lexicon (EmoLex), we
visualized the entanglement of emotions in the lexicon by using both projected
and raw GloVe word vectors. We showed that, in the proposed emotion space, we
were able to better disentangle emotions than using raw GloVe vectors alone. In
addition, we found that the sum vectors of different pairs of emotion words
successfully captured expressed human feelings in the EmoLex. For example, the
sum of two embedded word vectors expressing Joy and Trust which express Love
shared high similarity (similarity score .62) with the embedded vector
expressing Optimism. On the contrary, this sum vector was dissimilar
(similarity score -.19) with the the embedded vector expressing Remorse. In
this paper, we argue that through the proposed emotion space, arithmetic of
emotions is preserved in the word vectors. The affective representation
uncovered in emotion vector space could shed some light on how to help machines
to disentangle emotion expressed in word embeddings.
