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. 

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