Two-level feature selection for naive bayes with kernel density estimation in question classification based on Bloom's cognitive levels

2013 ◽  
Author(s):  
Catur Supriyanto ◽  
Norazah Yusof ◽  
Bowo Nurhadiono ◽  
Sukardi
Keyword(s):  
Feature Selection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Naive Bayes ◽  
Kernel Density ◽  
Naïve Bayes ◽  
Cognitive Levels ◽  
Question Classification ◽  
Selection For

scholarly journals A Comparative Performance Study of Classification Models for Opinion Mining

2022 ◽  
pp. 159-165
Author(s):  
S Raja Rajeswari ◽  
Dr. A. John Sanjeev Kumar
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Kernel Density ◽  
Naïve Bayes ◽  
Support Vector ◽  
Large Set ◽  
Performance Study ◽  
Comparative Performance

Opinion mining has become a major part in today's economy. People would want to know more about a product and the customers opinion before buying it. Companies would also want to know the opinions of the customers. Therefore, analyzing the customer’s opinion is important. A new customer would consider a product as good by analyzing the opinions of other customers. The opinions are collected from various areas, which include blogs, web forums, and product review sites. Classifying these large set of opinions requires a good classifier. In view of this, a comparative study of three classification techniques - Naive Bayes classifier with Kernel Density Estimation (KDE), Support Vector Machine (SVM), Decision Tree and KNN was made. To evaluate the classifier accuracy, precision, recall and F-measure techniques are used. Experimental results show that the Naive Bayes with Kernel Density Estimation (KDE) classifier achieved higher accuracy among others.

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