scholarly journals Analytical Approach on Indian Classical Raga Measures by Feature Extraction with EM and Naive Bayes

2014 ◽  
Vol 107 (6) ◽  
pp. 41-46 ◽  
Author(s):  
Akhilesh KSharma ◽  
Avinash Panwar ◽  
Prasun Chakrabarti
Keyword(s):  
Feature Extraction ◽  
Analytical Approach ◽  
Naive Bayes ◽  
Naïve Bayes

scholarly journals Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes

2020 ◽  
Vol 5 (3) ◽  
pp. 295
Author(s):  
Rahmawan Bagus Trianto ◽  
Andri Triyono ◽  
Dhika Malita Puspita Arum
Keyword(s):  
Feature Extraction ◽  
Naive Bayes ◽  
Automatic Classification ◽  
Naïve Bayes ◽  
Lack Of Information ◽  
N Gram ◽  
Bayes Algorithm ◽  
Online Product Ratings ◽  
Product Description

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.

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