A sub-discipline of Information Retrieval (IR) is opinion mining and the lexicon of computers is not concerned of the subject of the document, but about the opinion expressed. It has caused a large impact in the arena of academics and industry as it has a wide area of research and the applications are widespread. Feature selection is a vital step in opinion mining, as its individual feature decides the opinions expressed by the customers. Feature selection reduces the dimensionality of data by avoiding non-relevant features; it can be considered as a necessary and excellent process for data mining applications. In this study, feature subset is optimized through Particle Swarm Optimization (PSO) algorithm, Cuckoo Search (CS) algorithm and hybridized PSO-CS algorithm. Classification is done through Naïve bayes and K-Nearest Neighbours (KNN) classifiers. Feature extraction has its basis on Term Frequency-Inverse Document Frequency (TF-IDF). The accuracy of classification precision is increased by the reduction in size of feature subset and computational complexity.
Opinion mining analyses people’s opinions, evaluations, sentiments, attitudes, appraisals and emotions to entities like products, organizations, services, issues, individuals, topics, events and their attributes. It is a large problem space having high feature dimensionality. Feature extraction is important in opinion mining as customers do not usually express product opinions totally, but separately based on individual features. Two tasks should be accomplished in feature-based opinion mining. First, product features on which reviewers expressed opinions must be identified and extracted. Second, opinion orientation or polarities must be determined. Finally, opinion mining summarizes extracted features and opinions. In this work a novel wrapper based feature selection mechanism using concept based feature expansion is proposed. The wrapper based technique uses the principles of evolutionary algorithms.
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