Journal
KNOWLEDGE-BASED SYSTEMS
Volume 125, Issue -, Pages 116-135Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2017.03.020
Keywords
Sentiment analysis; Aspect term extraction; Feature selection; Ensemble; Conditional random field; Support vector machine; Maximum entropy; Particle swarm optimization
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In this paper we present a cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis. Aspect based sentiment analysis is performed in two steps, viz. aspect term extraction and sentiment classification. The pruned, compact set of features performs better compared to the baseline model that makes use of the complete set of features for aspect term extraction and sentiment classification. We further construct an ensemble based on PSO, and put it in cascade after the feature selection module. We use the features that are identified based on the properties of different classifiers and domains. As base learning algorithms we use three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Experiments for aspect term extraction and sentiment analysis on two different kinds of domains show the effectiveness of our proposed approach. (C) 2017 Elsevier B.V. All rights reserved.
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