Sentiment analysis: Bayesian Ensemble Learning

Sentiment analysis: Bayesian Ensemble Learning

Bayesian Ensemble Learning for Nonlinear Factor Analysis

An active research topic in machine learning is the development of model structures which would be rich enough to represent relevant aspects of the observations but would still allow efficient learning and inference. Linear factor analysis and related methods such as principal component analysis and independent component analysis are widely used feature extraction and data analysis techniques. ...

Improving Sentiment Analysis Through Ensemble Learning of Meta-level Features

In this research, the well-known microblogging site, Twitter, was used for a sentiment analysis investigation. We propose an ensemble learning approach based on the meta-level features of seven existing lexicon resources for automated polarity sentiment classification. The ensemble employs four base learners (a Two-Class Support Vector Machine, a Two-Class Bayes Point Machine, a Two-Class Logis...

Bayesian Ensemble Learning

We develop a Bayesian “sum-of-trees” model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting and inference are accomplished via an iterative backfitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting, each weak learner (i.e., each weak tree) contributes a small amount to the overall ...

Sentiment classification: The contribution of ensemble learning

Article history: Received 27 August 2012 Received in revised form 1 August 2013 Accepted 5 August 2013 Available online 15 August 2013