期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2018
卷号：9
期号：1
DOI：10.11591/ijece.v9i1.pp%p
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：Online product reviews has 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 performed well on the sentiment classification with Word2Vec model have accuracy value 0.70. However, the Word2vec model have the lowest accuracy value, 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 example to learn the word representation and place similar words into closer position.
关键词：Text Mining; Natural Language Processing;Sentiment Analysis; Support Vector Machine; Text Classification; Word2Vec; Word Embedding
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