Study on Opinion Mining Framework Using Proposed RB-Bayes Model for Text Classification

2021 ◽  
pp. 99-109
Author(s):  
Rajni Bhalla ◽  
Amandeep Bagga
Keyword(s):  
Text Classification ◽  
Opinion Mining ◽  
Bayes Model

scholarly journals Opinion mining framework using proposed RB-bayes model for text classication

2019 ◽  
Vol 9 (1) ◽  
pp. 477
Author(s):  
Rajni Bhalla ◽  
Amandeep Bagga
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Neural Systems ◽  
Data Sets ◽  
Bayes Method ◽  
Bayes Model ◽  
Improved Accuracy ◽  
Concealed Information ◽  
Better Than ◽  
Analyze Data

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

scholarly journals Opinion mining framework using proposed RB-bayes model for text classication

2019 ◽  
Vol 9 (1) ◽  
pp. 477
Author(s):  
Rajni Bhalla ◽  
Amandeep Bagga
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Neural Systems ◽  
Data Sets ◽  
Bayes Method ◽  
Bayes Model ◽  
Improved Accuracy ◽  
Concealed Information ◽  
Better Than ◽  
Analyze Data

<p><span lang="EN-US">Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.</span></p>

scholarly journals Using Collaborative Tagging for Text Classification: From Text Classification to Opinion Mining

Informatics ◽  
2013 ◽  
Vol 1 (1) ◽  
pp. 32-51 ◽  
Author(s):  
Eric Charton ◽  
Marie-Jean Meurs ◽  
Ludovic Jean-Louis ◽  
Michel Gagnon
Keyword(s):  
Text Classification ◽  
Opinion Mining ◽  
Collaborative Tagging

Study on the Method of Feature Selection Based on Hybrid Model for Text Classification

2012 ◽  
Vol 433-440 ◽  
pp. 2881-2886 ◽  
Author(s):  
Run Zhi Li ◽  
Yang Sen Zhang
Keyword(s):  
Feature Selection ◽  
Hybrid Model ◽  
Text Classification ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Selection Models ◽  
Bayes Model ◽  
Naïve Bayes Model

In this paper, we study on the problem of how to combine feature selection models in text classification ,and present a method through build the hybrid model for feature selection ,this hybrid model combined with advantage of four feature selection models (DF,MI, IG, CHI), then we use the Naive Bayes model as classifier to verify the effect of the hybrid feature selelction model ,and experiments shows that the hybrid model is correct and effective and get good performance in text classification.

Bayesian Diagnostic Probabilities without Assuming Independence of Symptoms

1991 ◽  
Vol 30 (01) ◽  
pp. 15-22 ◽  
Author(s):  
A. Gammerman ◽  
A. R. Thatcher
Keyword(s):  
Abdominal Pain ◽  
Acute Abdominal Pain ◽  
Final Diagnosis ◽  
Bayes Theorem ◽  
Bayes Method ◽  
Hospital Records ◽  
Computer Diagnosis ◽  
Bayes Model ◽  
Chi Squared ◽  
Past Data

The paper describes an application of Bayes’ Theorem to the problem of estimating from past data the probabilities that patients have certain diseases, given their symptoms. The data consist of hospital records of patients who suffered acute abdominal pain. For each patient the records showed a large number of symptoms and the final diagnosis, to one of nine diseases or diagnostic groups. Most current methods of computer diagnosis use the “Simple Bayes” model in which the symptoms are assumed to be independent, but the present paper does not make this assumption. Those symptoms (or lack of symptoms) which are most relevant to the diagnosis of each disease are identified by a sequence of chi-squared tests. The computer diagnoses obtained as a result of the implementation of this approach are compared with those given by the “Simple Bayes” method, by the method of classification trees (CART), and also with the preliminary and final diagnoses made by physicians.

Sign in / Sign up

Export Citation Format

Share Document