Multi-level particle swarm optimisation and its parallel version for parameter optimisation of ensemble models: a case of sentiment polarity prediction

Ensemble learning is increasingly used in sentiment analysis. Determining the parameter settings of ensemble models, however, is not easy. Besides its own parameters, an ensemble model has base-predictors that have their individual parameters. Some ensemble models use a specific base-predictor and could be optimised using standard metaheuristics such as the Particle Swarm Optimisation (PSO) approach. Optimising ensemble models with multiple base-predictor candidates is more complicated and challenging, as there are multiple options to choose from. We therefore propose Multi-Level PSO (ML-PSO) and Parallel ML-PSO (PML-PSO) to optimise the parameters of ensemble models, especially those with multiple base-predictors, for sentiment analysis. The idea is to utilise multiple PSOs as particles of the main PSO. The main PSO optimises ensemble-model parameters and determines the best base-predictor, whereas PSOs within it optimise the corresponding base-predictor’s parameters. Experimental results using Bagging Predictors as the underlying ensemble model show that ML-PSO can improve prediction accuracy, while PML-PSO is able to speed up the processing time and further improve the accuracy.