Word2Vec model for sentiment analysis of product reviews in Indonesian language

Abstract

Online product reviews have become a source of greatly valuable information for consumers in making purchase decisions and producers to improve their product and marketing strategies. However, it becomes more and more difficult for people to understand and evaluate what the general opinion about a particular product in manual way since the number of reviews available increases. Hence, the automatic way is preferred. One of the most popular techniques is using machine learning approach such as Support Vector Machine (SVM). In this study, we explore the use of Word2Vec model as features in the SVM based sentiment analysis of product reviews in Indonesian language. The experiment result show that SVM can performs well on the sentiment classification task using any model used. However, the Word2vec model has the lowest accuracy (only 0.70), compared to other baseline method including Bag of Words model using Binary TF, Raw TF, and TF.IDF. This is because only small dataset used to train the Word2Vec model. Word2Vec need large examples to learn the word representation and place similar words into closer position.