Sentiment analysis based on improved pre-trained word embeddings

Abstract

Sentiment analysis is a fast growing area of research 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 large corpus of texts for training and generating exact vectors. As a result, because of the small size of some corpora, researcher often have to use pre-trained word embeddings which were trained on other large text corpora 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, word position algorithm and Word2Vec/GloVe methods. We tested the accuracy of our method via different deep learning models and benchmark sentiment datasets. Our experiment results show that Improved Word Vectors (IWV) are very effective for sentiment analysis.