Hybrid optimization for feature selection in opinion mining

  • Authors

    • Prasanna Moorthi N.
    • Mathivanan V
    2017-12-31
    https://doi.org/10.14419/ijet.v7i1.3.9668
  • Opinion Mining, Feature Selection, Particle Swarm Optimization (PSO), Cuckoo Search (CS), Term Frequency-Inverse Document Frequency (TF-IDF), Naïve Bayes and K-Nearest Neighbours (KNN).

  • A sub-discipline of Information Retrieval (IR) is opinion mining and the lexicon of computers is not concerned of the subject of thedocument, 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 con-sidered 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 DocumentFrequency (TF-IDF).The accuracy of classification precision is increased by the reduction in size of feature subset and computational com-plexity.

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    Moorthi N., P., & V, M. (2017). Hybrid optimization for feature selection in opinion mining. International Journal of Engineering & Technology, 7(1.3), 112-117. https://doi.org/10.14419/ijet.v7i1.3.9668