Abstract
This work proposes a semi-supervised sentiment classification method. Our method utilizes spectral clustering-based algorithm to improve the sentiment classification accuracy. We adopt a spectral clustering algorithm to map sentiment units in consumer reviews into new features which are extended into the original feature space. One sentiment classifier is built on the features in the original training space, and the original training features combined with the extended features are used to train the other sentiment classifier. The two basic sentiment classifiers together form the final sentiment classifier through selecting instances in the unlabeled data set into the training data set. Experimental results show that our proposed method has better performance than Self-learning SVM-based sentiment classification method.
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Li, S., Hao, J. (2012). Spectral Clustering-Based Semi-supervised Sentiment Classification. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_23
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DOI: https://doi.org/10.1007/978-3-642-35527-1_23
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