  Sentiment analysis is one of the well-known tasks and fast growing research
areas in natural language processing (NLP) and text classifications. This
technique has become an essential part of a wide range of applications
including politics, business, advertising and marketing. There are various
techniques for sentiment analysis, but recently word embeddings methods have
been widely used in sentiment classification tasks. Word2Vec and GloVe are
currently among the most accurate and usable word embedding methods which can
convert words into meaningful vectors. However, these methods ignore sentiment
information of texts and need a huge corpus of texts for training and
generating exact vectors which are used as inputs of deep learning models. As a
result, because of the small size of some corpuses, researcher often have to
use pre-trained word embeddings which were trained on other large text corpus
such as Google News with about 100 billion words. The increasing accuracy of
pre-trained word embeddings has a great impact on sentiment analysis research.
In this paper we propose a novel method, Improved Word Vectors (IWV), which
increases the accuracy of pre-trained word embeddings in sentiment analysis.
Our method is based on Part-of-Speech (POS) tagging techniques, lexicon-based
approaches and Word2Vec/GloVe methods. We tested the accuracy of our method via
different deep learning models and sentiment datasets. Our experiment results
show that Improved Word Vectors (IWV) are very effective for sentiment
analysis.
