Nowadays, the exponential diffusion of information forces Machine Learning algorithms to take relations into account in addition to data, which are no longer independent. We propose a Bayesian ensemble learning methodology named Relational Bayesian Model Averaging (RBMA) which, in addition to a probabilistic ensemble voting, takes relations into account. We tested the RBMA on a benchmark dataset for Sentiment Analysis in social networks and we compared it with its previous non-relational variant and we show that the introduction of relations significantly improves the performance of classification. Moreover, we propose a model for making predictions when new data becomes available modifying and increasing the underneath graph of relations on which the RBMA was trained.

Baldi, M., Fersini, E., Messina, E. (2020). Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks. In Machine Learning, Optimization, and Data Science (pp.285-296). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-64583-0_27].

Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks

Baldi M. M.
;
Fersini E.;Messina E.
2020

Abstract

Nowadays, the exponential diffusion of information forces Machine Learning algorithms to take relations into account in addition to data, which are no longer independent. We propose a Bayesian ensemble learning methodology named Relational Bayesian Model Averaging (RBMA) which, in addition to a probabilistic ensemble voting, takes relations into account. We tested the RBMA on a benchmark dataset for Sentiment Analysis in social networks and we compared it with its previous non-relational variant and we show that the introduction of relations significantly improves the performance of classification. Moreover, we propose a model for making predictions when new data becomes available modifying and increasing the underneath graph of relations on which the RBMA was trained.
paper
Ensemble learning; Relational classifiers; Sentiment analysis
English
6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020
2020
Machine Learning, Optimization, and Data Science
978-3-030-64582-3
2020
12565
285
296
none
Baldi, M., Fersini, E., Messina, E. (2020). Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks. In Machine Learning, Optimization, and Data Science (pp.285-296). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-64583-0_27].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/305706
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