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.

整体学习越来越多地用于情感分析中。但是，确定集成模型的参数设置并不容易。除了其自身的参数外，集成模型还具有具有各自参数的基础预测变量。一些集成模型使用特定的基本预测变量，并且可以使用标准的元启发法（例如粒子群优化（PSO）方法）进行优化。由于有多种选择，使用多个基础预测变量的候选者来优化集成模型更加复杂和具有挑战性。因此，我们提出了多级PSO（ML-PSO）和并行ML-PSO（PML-PSO）来优化整体模型的参数，尤其是具有多个基础预测变量的整体模型的参数，以进行情感分析。这个想法是利用多个PSO作为主要PSO的粒子。主要的PSO优化集成模型参数并确定最佳的基础预测器，而其中的PSO则优化相应的基础预测器的参数。使用装袋预测器作为基础集成模型的实验结果表明，ML-PSO可以提高预测精度，而PML-PSO可以加快处理时间并进一步提高精度。