One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition, we propose an heuristic aimed at evaluating the a priori contribution of each model to the classification task. Experimental results on different datasets show that Bayesian Model Averaging, together with the proposed heuristic, outperforms traditional classification methods and the well known Majority Voting mechanism. © 2013 Springer-Verlag Berlin Heidelberg.
Pozzi, F., Fersini, E., Messina, V. (2013). Bayesian Model Averaging and Model Selection for Polarity Classification. In Natural Language Processing and Information Systems (pp.189-200). Springer [10.1007/978-3-642-38824-8_16].
Bayesian Model Averaging and Model Selection for Polarity Classification
POZZI, FEDERICO ALBERTO;FERSINI, ELISABETTA;MESSINA, VINCENZINA
2013
Abstract
One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition, we propose an heuristic aimed at evaluating the a priori contribution of each model to the classification task. Experimental results on different datasets show that Bayesian Model Averaging, together with the proposed heuristic, outperforms traditional classification methods and the well known Majority Voting mechanism. © 2013 Springer-Verlag Berlin Heidelberg.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
