Abstract
More and more product reviews emerge on E-commerce sites and microblog systems nowadays. This information is useful for consumers to know the others’ opinion on the products before purchasing, or companies who want to learn the public sentiment of their products. In order to effectively utilize this information, this paper has done some sentiment analysis on these multi-source reviews. For one thing, a binary classification framework based on the aspects of product is proposed. Both explicit and implicit aspect is considered and multiple kinds of feature weighing and classifiers are compared in our framework. For another, we use several machine learning algorithms to classify the product reviews in microblog systems into positive, negative and neutral classes, and find OVA-SVMs perform best. Part of our work in this paper has been applied in a Chinese Product Review Mining System.
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Jin, H., Huang, M., Zhu, X. (2012). Sentiment Analysis with Multi-source Product Reviews. In: Huang, DS., Jiang, C., Bevilacqua, V., Figueroa, J.C. (eds) Intelligent Computing Technology. ICIC 2012. Lecture Notes in Computer Science, vol 7389. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31588-6_39
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DOI: https://doi.org/10.1007/978-3-642-31588-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31587-9
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