Given a query word 21) and another word 21/ we obtain their vector representations gbw and gwa, and evaluate their cosine similarity as 8(gbw, gwa) = By assessing the similarity of 212 with all other words 212’, we can find the words deemed most similar by the model.

This component of the model uses the vector representation of words to predict the sentiment annotations on contexts in which the words appear.

This causes words expressing similar sentiment to have similar vector representations .

The energy function uses a word representation matrix R E R“ X M) where each word 21) (represented as a one-on vector) in the vocabulary V has a 6-dimensional vector representation gbw = Rw corresponding to that word’s column in R. The random variable 6 is also a B-dimensional vector, 6 E R5 which weights each of the 6 dimensions of words’ representation vectors.

For each latent topic T, the model learns a conditional distribution p(w|T) for the probability that word 21) occurs in T. One can obtain a k:-dimensional vector representation of words by first training a k-topic model and then filling the matrix with the p(w|T) values (normalized to unit length).