The Use of Word2vec Model in Sentiment Analysis: A Survey

Abstract

Sentiment analysis is an area that gains wide interest from research because of its importance and advantages in various fields. Different approaches and techniques are used to classify the sentiment of texts. Word embedding is one of the effective methods that represent aspects of word meaning and help to improve sentiment classification accuracy. Word2vec is well-known and widely used in learning word embedding that includes two models: Skip-Gram (SG) model and Continuous Bag-of-Words model (CBOW). Some of the studies use one of these models and other studies use both of them. In this survey, we highlight the latest studies on using the Word2vec model for sentiment analysis and its role in improving sentiment classification accuracy.