Semantic-Based Unsupervised Hybrid Technique for Opinion Targets Extraction from Unstructured Reviews

Abstract

Opinion target identification is an important task of opinion mining problem. Several approaches have been employed for this task that can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data which need manual work and are mostly domain dependent. Unsupervised technique is most popularly used due to its two main advantages: domain independent and no need of training data. This paper presents optimization of unsupervised likelihood ratio test (LRT) technique for opinion targets identification from unstructured reviews. The LRT approach basically depends on frequently observed features and hence do not predict infrequent features (IFF). This paper exploits WordNet lexical resource to identify IFF based on semantic relatedness to the frequent features in review documents which improves the overall accuracy of features extraction algorithms. Empirical results show the validity of the proposed technique.