Location of Repository

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

By Yulan He

Abstract

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text

Year: 2011
OAI identifier: oai:oro.open.ac.uk:28543
Provided by: Open Research Online

Suggested articles

Preview


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.