An Improved Feature Selection Based on Naive Bayes with Kernel Density Estimator for Opinion Mining

Abstract

Opinion mining has gained much attention in the recent years due to the rapid growth of social media. It is a task of analyzing customer reviews to make decisions by classifying the reviews into positive or negative. These text reviews have high dimensions that lead to the curse of dimensionality. To handle this high dimension of text data, improved gain ratio is proposed to select the features with the highest ranking. Naїve Bayes classifier with kernel density function is used to evaluate the feature set. The Naїve Bayes classifier with Kernel density estimation is a nonparametric classifier that computes the probability density function based on the kernel estimator. This classifier produces higher accuracy in various benchmarking datasets.

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Correspondence to Raja Rajeswari Sethuraman.

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Sethuraman, R.R., Athisayam, J.S.K. An Improved Feature Selection Based on Naive Bayes with Kernel Density Estimator for Opinion Mining. Arab J Sci Eng (2021). https://doi.org/10.1007/s13369-021-05381-5

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Keywords

  • Opinion mining
  • Feature selection
  • Filter method
  • Naïve Bayes
  • Kernel density estimation