Sentiment Analysis of Online Media

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Title: Sentiment Analysis of Online Media
Authors: Salter-Townshend, Michael
Murphy, Thomas Brendan
Permanent link: http://hdl.handle.net/10197/3964
Date: 18-Dec-2012
Abstract: A joint model for annotation bias and document classification is presented in the context of media sentiment analysis. We consider an Irish online media data set comprising online news articles with user annotations of negative, positive or irrelevant impact on the Irish economy. The joint model combines a statistical model for user annotation bias and a Naive Bayes model for the document terms. An EM algorithm is used to estimate the annotation bias model, the unobserved biases in the user annotations, the classifier parameters and the sentiment of the articles. The joint modeling of both the user biases and the classifier is demonstrated to be superior to estimation of the bias followed by the estimation of the classifier parameters.
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2012 Springer
Subject LCSH: User-generated content--Classification
Human computation
News Web sites
Mass media--Objectivity
Language: en
Status of Item: Peer reviewed
Is part of: Lausen, B., van del Poel, D. and Ultsch, A. (eds.). Algorithms from and for Nature and Life. Studies in Classification, Data Analysis, and Knowledge Organization
Conference Details: GfKl 2011: Joint Conference of the German Classification Society (GfKl) and the German Association for Pattern Recognition (DAGM) August 31 to September 2, 2011 and the IFCS 2011: Symposium of the International Federation of Classification Societies (IFCS) August 30, 2011, Frankfurt am Main, Germany
Appears in Collections:Computer Science Research Collection
Mathematics and Statistics Research Collection
Clique Research Collection
CASL Research Collection

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