Decision tree combined with PSO-based feature selection for sentiment analysis
Rifkie Primartha, Bayu Adhi Tama, Azhary Arliansyah, Kanda Januar Miraswan · Journal of Physics Conference Series · 2019
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.