Decision tree combined with pso-based feature selection for sentiment analysis

Abstract

Sentiment analysis can be considered as a classification task in natural language processing as it harnesses classification algorithm to predict a particular class in a text data. In the classification task, feature extraction is a process to extract the features of the data so that it can be used as the input of the classification algorithm. However, not all features are particularly relevant for a classifier. Irrelevant features might significantly decrease the performance of classification algorithm. This paper proposes a PSO-based feature selection, combined with decision tree algorithm (PSO-C4.5) for sentiment analysis. The PSO-C4.5 is validated on a private data set, which is a sentiment data set about online transportation in Indonesia. The proposed method considerably enhances the performance of decision tree in comparison with the baseline. © Published under licence by IOP Publishing Ltd.