A sentiment analysis model for hotel reviews based on supervised learning

• Published in: 2011 International Conference on Machine Learning and Cybernetics Vol. 3; pp. 950 - 954
• Main Authors: Han-Xiao Shi, Xiao-Jun Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Computational linguistics; Data mining; Machine learning; Online reviews; Presses; Semantics; Sentiment classification; supervised machine learning; Support vector machines; Training
• ISBN: 9781457703058; 145770305X
• ISSN: 2160-133X
• DOI: 10.1109/ICMLC.2011.6016866

As the widespread use of computers and the high-speed development of the Internet, E-Commerce has already penetrated as a part of our daily life. For a popular product, there are a large number of reviews. This makes it difficult for a potential customer to make an informed decision on purchasing the product, as well as for the manufacturer of the product to keep track and to manage customer opinions. In this paper, we pay attention to online hotel reviews, and propose a supervised machine learning approach using unigram feature with two types of information (frequency and TF-IDF) to realize polarity classification of documents. As shown in our experimental results, the information of TF-IDF is more effective than frequency.