Unsupervised sentiment analysis with a simple and fast Bayesian model using Part-of-Speech feature selection

Christian Scheible, Hinrich Schütze
2012 Conference on Natural Language Processing  
Unsupervised Bayesian sentiment analysis often uses models that are not well motivated. Mostly, extensions of Latent Dirichlet Analysis (LDA) are applied -effectively modeling latent class distributions over words instead of documents. We introduce a Bayesian, unsupervised version of Naive Bayes for sentiment analysis and show that it offers superior accuracy and inference speed.
dblp:conf/konvens/ScheibleS12 fatcat:xssv5byepraupchri3tjk2vgka