Multi-PSO based Classifier Selection and Parameter Optimisation for Sentiment Polarity Prediction
Gregorius Satia Budhi, Raymond Chiong, Zhongyi Hu, Ilung Pranata, Sandeep Dhakal · 2018
In the big data era, machine learning algorithms are extensively used for sentiment polarity prediction. However, identifying the correct machine learning algorithm and its parameter settings for the problem at hand can be a difficult task. We propose a system based on Particle Swarm Optimisation (PSO) to find the best machine learning algorithm and optimise its parameters for sentiment polarity prediction. The system’s design consists of two layers, namely a multi-PSO layer and a knockout layer. From experimental results, we find that each PSO in the multi-PSO layer could optimise the parameters of the classifiers processed. Overall, the system is able to determine the best classifier from the collection of processed classifiers and also provide quasi-optimal parameters for the classifier to predict the sentiment polarity of customer reviews.