About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
WWW 2013
Conference paper
Incorporating author preference in sentiment rating prediction of reviews
Abstract
Traditional works in sentiment analysis do not incorporate author preferences during sentiment classification of reviews. In this work, we show that the inclusion of author preferences in sentiment rating prediction of reviews improves the correlation with ground ratings, over a generic author independent rating prediction model. The overall sentiment rating prediction for a review has been shown to improve by capturing facet level rating. We show that this can be further developed by considering author preferences in predicting the facet level ratings, and hence the overall review rating. To the best of our knowledge, this is the first work to incorporate author preferences in rating prediction.