  People use the world wide web heavily to share their experience with entities
such as products, services, or travel destinations. Texts that provide online
feedback in the form of reviews and comments are essential to make consumer
decisions. These comments create a valuable source that may be used to measure
satisfaction related to products or services. Sentiment analysis is the task of
identifying opinions expressed in such text fragments. In this work, we develop
two methods that combine different types of word vectors to learn and estimate
polarity of reviews. We develop average review vectors from word vectors and
add weights to this review vectors using word frequencies in positive and
negative sensitivity-tagged reviews. We applied the methods to several datasets
from different domains that are used as standard benchmarks for sentiment
analysis. We ensemble the techniques with each other and existing methods, and
we make a comparison with the approaches in the literature. The results show
that the performances of our approaches outperform the state-of-the-art success
rates.
