Relational Bayesian Model Averaging for Sentiment Analysis in Social Networks

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.