  An important part of the information gathering and data analysis is to find
out what people think about, either a product or an entity. Twitter is an
opinion rich social networking site. The posts or tweets from this data can be
used for mining people's opinions. The recent surge of activity in this area
can be attributed to the computational treatment of data, which made opinion
extraction and sentiment analysis easier. This paper classifies tweets into
positive and negative sentiments, but instead of using traditional methods or
preprocessing text data here we use the distributed representations of words
and sentences to classify the tweets. We use Long Short Term Memory (LSTM)
Networks, Convolutional Neural Networks (CNNs) and Artificial Neural Networks.
The first two are used on Distributed Representation of words while the latter
is used on the distributed representation of sentences. This paper achieves
accuracies as high as 81%. It also suggests the best and optimal ways for
creating distributed representations of words for sentiment analysis, out of
the available methods.
