Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/77957
Title: A hybrid method for cross-domain sentiment classification using multiple sources
Authors: Fang, F.
Datta, A. 
Dutta, K.
Keywords: Business intelligence
Machine learning
Sentiment analysis
Issue Date: 2012
Source: Fang, F.,Datta, A.,Dutta, K. (2012). A hybrid method for cross-domain sentiment classification using multiple sources. International Conference on Information Systems, ICIS 2012 1 : 720-733. ScholarBank@NUS Repository.
Abstract: Sentiment classification is one of the most extensively studied problems in sentiment analysis and supervised learning methods, which require labeled data for training, have been proven quite effective. However, supervised methods assume that the training domain and the testing domain share the same distribution; otherwise, accuracy drops dramatically. Although this does not pose problems when training data are readily available, in some circumstances, labeled data is quite expensive to acquire. For instance, if we want to detect sentiment from Tweets or Facebook comments, the only way to acquire is to manually label it and thus prohibitively burdensome and timeconsuming. In this paper, we propose a hybrid approach that integrates the sentiment information from multiple source domains labeled data and a set of preselected sentiment words to solve this problem. The experimental results suggest that our method statistically outperforms the state of the art and even surpasses the in-domain gold standard in some cases.
Source Title: International Conference on Information Systems, ICIS 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/77957
ISBN: 9781627486040
Appears in Collections:Staff Publications

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