scholarly journals Less Sparse Feature Set with Meta Heuristic Weighted Classifier for Tweet Sentiment Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 1328-1334
Keyword(s):  
Naive Bayes ◽  
Hybrid Approach ◽  
Naïve Bayes ◽  
Sentiment Classification ◽  
Bayes Classifier ◽  
Hybrid Approaches ◽  
Machine Leaning ◽  
Feature Weight ◽  
Precision And Accuracy ◽  
Better Than

Twitter using Machine Leaning Techniques has been done. While consideration Bigram, Unigram,. SVM and naïve Bayes classifier which hybrid with PSO and ACO for effective feature weight. In Fig. 4.9 compare all experiment by on graph which shows that SVM_ACO and SVM_PSO better perform than SVM. NB_ACO and NB_PSO perform better than NB but if compare between hybrid approaches then SVM_PSO show 81.80% accuracy,85% precision and 80% recall. IN case of naïve Bayes NB_PSO 76.93% accuracy,76.24 precision and 82.55% recall, so experiments conclude that Naive Bayes improve recall and SVM improve precision and accuracy when use as hybrid approach.

scholarly journals Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Qingchao Liu ◽  
Jian Lu ◽  
Shuyan Chen ◽  
Kangjia Zhao
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
Classifier Ensemble ◽  
Optimal Threshold ◽  
Incident Detection ◽  
Bayes Classifier ◽  
Traffic Incident ◽  
Better Than

This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection. The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results. However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process. To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection. In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents. In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree. The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators. More importantly, the performances of the NB classifier ensemble are very stable.

scholarly journals Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

2019 ◽  
Vol 9 (5) ◽  
pp. 4452 ◽  
Author(s):  
Chaithra V. D
Keyword(s):  
Social Media ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Hybrid Approach ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Video Sharing ◽  
Social Media Site

<p align="justify">Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%.</p>

scholarly journals Ensemble Classification Method for Credit Card Fraud Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 423-427
Keyword(s):  
Execution Time ◽  
Credit Card ◽  
Naive Bayes ◽  
Research Work ◽  
Hybrid Approach ◽  
Fraud Detection ◽  
Naïve Bayes ◽  
Ensemble Classification ◽  
Bayes Classifier ◽  
Proposed Model

Credit card frauds are on the rise and are getting smarter with the passage of time. Usually, fraudulent transactions are conducted by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. In the existing work, it has been found that the researchers have utilized Voting based method to identify credit card frauds. The problem with voting based method is that they are more complex and more time consuming. In this research work, a hybrid approach based on KNN and Naive Bayes for the detection of credit card frauds. KNN will be used as the base classifier and it will return predicted result. The predicted result will be provided as input to the Naive Bayes classifier which will generate the final result. The proposed model will be compared with existing techniques and the results are analyzed in terms of recall, precision, accuracy and execution time.

scholarly journals Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House

2020 ◽  
Vol 1 (2) ◽  
pp. 46
Author(s):  
Nurhadi Wijaya
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Digital System ◽  
Bayes Classifier ◽  
Chi Square ◽  
Auc Value ◽  
Measure Comparison ◽  
Better Than ◽  
Comparison Data

Occupancy status is one indicator of the rehabilitation and reconstruction program to support eruption victims in Indonesia. It needs to establish the rehabilitation and reconstruction in digital system with structured database. In this paper, we provide dataset 2,146 occupied and 370 unoccupied houses. We utilize a naive Bayes classifier to classify the objects and implement a chi-square algorithm to measure comparison data to actual observed data. This research uses a combination of Naive Bayes and Chi-Square by applying weighting to the dataset attributes. Our study conclude that the combination of the algorithms can achieve a promosing result in classifying the occupancy houses status. The combination of the proposed technique gain 89.59% accuracy and ROC-AUC value 0.839. Therefore, our approach is better than the standard Naive Bayes without combination with the Chi-Square approach

scholarly journals A Multi-Level Naïve Bayes Classifier for Sentiment Classification

2012 ◽  
Vol 6-7 ◽  
pp. 553-560
Author(s):  
Jun Zheng Shi ◽  
Lei Guo ◽  
Shi Min Wei
Keyword(s):  
Naive Bayes ◽  
Rapid Development ◽  
Naïve Bayes ◽  
Sentiment Classification ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Depth Study ◽  
Multi Level

A great demand of sentiment classification comes with the rapid development of the internet. At present, the methods about sentiment classification based on machine learning have been widely used. The sentiment classification is a more difficult task, which needs more in-depth study than the traditional topic-based classification method [1]. Naïve Bayesian classifier is widely used in text classification. However, it requires two basic assumptions as its prerequisite and the performance would have been poor if these two were dissatisfied. We propose a multi-level naïve Bayes classifier to make up the deficiency of the traditional naïve Bayes classifier. The research below shows that the multi-level naïve Bayes classifier gets better performance than the traditional naïve Bayes classifier on the sentiment classification of movie reviews.

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