  Automated sentiment analysis and opinion mining is a complex process
concerning the extraction of useful subjective information from text. The
explosion of user generated content on the Web, especially the fact that
millions of users, on a daily basis, express their opinions on products and
services to blogs, wikis, social networks, message boards, etc., render the
reliable, automated export of sentiments and opinions from unstructured text
crucial for several commercial applications. In this paper, we present a novel
hybrid vectorization approach for textual resources that combines a weighted
variant of the popular Word2Vec representation (based on Term Frequency-Inverse
Document Frequency) representation and with a Bag- of-Words representation and
a vector of lexicon-based sentiment values. The proposed text representation
approach is assessed through the application of several machine learning
classification algorithms on a dataset that is used extensively in literature
for sentiment detection. The classification accuracy derived through the
proposed hybrid vectorization approach is higher than when its individual
components are used for text represenation, and comparable with
state-of-the-art sentiment detection methodologies.
