  Sentiment polarity of tweets, blog posts or product reviews has become highly
attractive and is utilized in recommender systems, market predictions, business
intelligence and more. Deep learning techniques are becoming top performers on
analyzing such texts. There are however several problems that need to be solved
for efficient use of deep neural networks on text mining and text polarity
analysis. First, deep neural networks need to be fed with data sets that are
big in size as well as properly labeled. Second, there are various
uncertainties regarding the use of word embedding vectors: should they be
generated from the same data set that is used to train the model or it is
better to source them from big and popular collections? Third, to simplify
model creation it is convenient to have generic neural network architectures
that are effective and can adapt to various texts, encapsulating much of design
complexity. This thesis addresses the above problems to provide methodological
and practical insights for utilizing neural networks on sentiment analysis of
texts and achieving state of the art results. Regarding the first problem, the
effectiveness of various crowdsourcing alternatives is explored and two
medium-sized and emotion-labeled song data sets are created utilizing social
tags. To address the second problem, a series of experiments with large text
collections of various contents and domains were conducted, trying word
embeddings of various parameters. Regarding the third problem, a series of
experiments involving convolution and max-pooling neural layers were conducted.
Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
