Authors: Lin Shang Zhe Zhou Xing Liu
Publish Date: 2016/03/10
Volume: 20, Issue: 10, Pages: 3821-3834
Abstract
Sentiment classification is one of the important tasks in text mining which is to classify documents according to their opinion or sentiment Documents in sentiment classification can be represented in the form of feature vectors which are employed by machine learning algorithms to perform classification For the feature vectors the feature selection process is necessary In this paper we will propose a feature selection method called fitness proportionate selection binary particle swarm optimization FBPSO Binary particle swarm optimization BPSO is the binary version of particle swam optimization and can be applied to feature selection domain FBPSO is a modification of BPSO and can overcome the problems of traditional BPSO including unreasonable update formula of velocity and lack of evaluation on every single feature Then some detailed changes are made on the original FBPSO including using fitness sum instead of average fitness in the fitness proportionate selection step The modified method is thus called fitness sum proportionate selection binary particle swarm optimization FSBPSO Moreover further modifications are made on the FSBPSO method to make it more suitable for sentiment classificationoriented feature selection domain The modified method is named as SCOFSBPSO where SCO stands for “sentiment classificationoriented” Experimental results show that in benchmark datasets original FBPSO is superior to traditional BPSO in feature selection performance and FSBPSO outperforms original FBPSO Besides in sentiment classification domain SCOFSBPSO which is modified specially for sentiment classification is superior to traditional feature selection methods on subjective consumer review datasets
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