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