A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Unsupervised sentiment analysis with a simple and fast Bayesian model using Part-of-Speech feature selection
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