  Word embeddings are representations of individual words of a text document in
a vector space and they are often use- ful for performing natural language pro-
cessing tasks. Current state of the art al- gorithms for learning word
embeddings learn vector representations from large corpora of text documents in
an unsu- pervised fashion. This paper introduces SWESA (Supervised Word
Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via
word embeddings. SWESA leverages document label infor- mation to learn vector
representations of words from a modest corpus of text doc- uments by solving an
optimization prob- lem that minimizes a cost function with respect to both word
embeddings as well as classification accuracy. Analysis re- veals that SWESA
provides an efficient way of estimating the dimension of the word embeddings
that are to be learned. Experiments on several real world data sets show that
SWESA has superior per- formance when compared to previously suggested
approaches to word embeddings and sentiment analysis tasks.
