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Arabian Journal for Science and Engineering
Erschienen in: Arabian Journal for Science and Engineering 4/2021

13.02.2021 | Research Article-Computer Engineering and Computer Science

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

verfasst von: Raja Rajeswari Sethuraman, John Sanjeev Kumar Athisayam

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 4/2021

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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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Metadaten
Titel
An Improved Feature Selection Based on Naive Bayes with Kernel Density Estimator for Opinion Mining
verfasst von
Raja Rajeswari Sethuraman
John Sanjeev Kumar Athisayam
Publikationsdatum
13.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Arabian Journal for Science and Engineering / Ausgabe 4/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-05381-5

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