scholarly journals Quantitative Analysis Powered by Naïve Bayes Classifier Algorithm to Data-Related Publications Social-Scientific Network

2020 ◽  
Vol 8 (6) ◽  
pp. 205-217
Author(s):  
Tobias Sombra ◽  
Rose Santini ◽  
Emerson Morais ◽  
Walmir Couto ◽  
Alex Zissou ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Exploratory Data Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Feature Weighting ◽  
Research Network ◽  
Bayes Classifier ◽  
Exploratory Data ◽  
Bayes Algorithm ◽  
Social Scientific

Quantitative evaluation of a dataset can play an important role in pattern recognition of technical-scientific research involving behavior and dynamics in social networks. As an example, are the adaptive feature weighting approaches by naive Bayes text algorithm. This work aims to present an exploratory data analysis with a quantitative approach that involves pattern recognition using the Mendeley research network; to identify logics given the popularity of document access. To better analyze the results, the work was divided into four categories, each with three subcategories, that is, five, three, and two output classes. The name for these categories came up due to data collection, which also presented documents with open access, dismembering proceedings, and journals for two more categories. As a result, the performance for the test examples showed a lower error rate related to the subcategory two output classes in the criterion of popularity by using the naive Bayes algorithm in Mendeley.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

Analisa Topik Pendidikan Dalam Al-Quran dengan Pendekatan Text Mining

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bustami Yusuf ◽  
Muhammad Zaeki ◽  
Hendri Ahmadian ◽  
Khairan Ar ◽  
Sri Wahyuni
Keyword(s):  
Evaluation Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Sources Of Knowledge ◽  
Scientific Disciplines ◽  
Study Results ◽  
The World ◽  
The Subject ◽  
Bayes Algorithm

Education is one of the sciences that makes humans much better by learning various scientific disciplines. Al-Quran is one of the sources of knowledge that is believed by Muslims around the world. Because technology has penetrated almost every domain of our lives , including the world of education. Thus, the authors make technology as tool  for researching educational topics in Al-Quran by implementing text exploration .The research was carried out by making some basic words that were related to the subject of education as the keywords in this study. The keywords are “Ajar”, “Bicara”, “Cipta”, “Dengar”, “Ingat” and “Lihat”. Then, the authors implemented the Naïve Bayes Classifier algorithm. To test and evaluate the results, the author used two methods, i.e. recall and precision. The study results are the keyword “cipta” by 3.05 %, “Ingat” 2.25 %, “Ajar” 1.96 %,“Lihat” 0.82 %, finally “Dengar” 0.62% and “Bicara” 0.34% with  total  weight of 3,516 words that  have been filtered. The overall percentage of the results is 9.04% of the total number of words 38,761 in the Al-Quran. For the Naïve Bayes algorithm evaluation method,  the recall and precision scores are 0.605 and 0.366, respectively.

scholarly journals Implemented PSO-NBC and PSO-SVM to Help Determine Status of Volcanoes

2019 ◽  
Vol 9 (2) ◽  
pp. 97
Author(s):  
Firman Tempola
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Swarm Optimization ◽  
Attribute Weights ◽  
The Status ◽  
Bayes Algorithm

<p class="JGI-AbstractIsi">This research is a continuation of previous research that applied the Naive Bayes classifier algorithm to predict the status of volcanoes in Indonesia based on seismic factors. There are five attributes used in predicting the status of volcanoes, namely the status of the normal, standby and alerts. The results Showed the accuracy of the resulted prediction was only 79.31%, or fell into fair classification. To overcome these weaknesses and in order to increase accuracy, optimization is done by giving criteria or attribute weights using particle swarm optimization. This research compared the optimization of Naive Bayes algorithm to vector machine support using particle swarm optimization. The research found improvement on system after application of PSO-NBC to that of 91.3 % and 92.86% after applying PSO-SVM.</p>

scholarly journals Opinion Mining Classification using Naive Bayes Algorithm

2020 ◽  
Vol 9 (5) ◽  
pp. 495-498
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Product Review ◽  
Online Services ◽  
Recent Advancement ◽  
Bayes Algorithm

With the recent advancement in the field of online services, the importance of a review for a product has also gone up. In this paper we focus on the aspect of reducing the time and effort for the user by recommending the best product to him. For this to be achieved, this paper proposes a Naive Bayes Classifier which labels the reviews accurately and combines the reviews to give a final rating to the product. The amazon product review data consisting of both negative and positive reviews was used for training and testing purposes. The model’s performance is evaluated, and results are analysed.

scholarly journals Boosted Gaussian Bayes Classifier and its application in bank credit scoring

2018 ◽  
Vol 2 (2) ◽  
pp. 131
Author(s):  
Anaïs Pizzo ◽  
Pascal Teyssere ◽  
Long Vu-Hoang
Keyword(s):  
Naive Bayes ◽  
Credit Scoring ◽  
Naïve Bayes ◽  
Bank Credit ◽  
Bayes Classifier ◽  
Data Bases ◽  
Creative Commons ◽  
Bayes Algorithm ◽  
Open Access Article ◽  
Data Banks

With the explosion of computer science in the last decade, data banks and networksmanagement present a huge part of tomorrows problems. One of them is the development of the best classication method possible in order to exploit the data bases. In classication problems, a representative successful method of the probabilistic model is a Naïve Bayes classier. However, the Naïve Bayes effectiveness still needs to be upgraded. Indeed, Naïve Bayes ignores misclassied instances instead of using it to become an adaptive algorithm. Different works have presented solutions on using Boosting to improve the Gaussian Naïve Bayes algorithm by combining Naïve Bayes classier and Adaboost methods. But despite these works, the Boosted Gaussian Naïve Bayes algorithm is still neglected in the resolution of classication problems. One of the reasons could be the complexity of the implementation of the algorithm compared to a standard Gaussian Naïve Bayes. We present in this paper, one approach of a suitable solution with a pseudo-algorithm that uses Boosting and Gaussian Naïve Bayes principles having the lowest possible complexity. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

2018 ◽  
Vol 3 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Arya Kusuma ◽  
De Rosal Ignatius Moses Setiadi ◽  
M. Dalvin Marno Putra
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Bayes Classifier ◽  
Nutritional Content ◽  
Classification Technique ◽  
Computational Speed ◽  
Testing Data ◽  
Bayes Algorithm ◽  
Histogram Feature

Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

scholarly journals Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier

2021 ◽  
Vol 5 (2) ◽  
pp. 233-242
Author(s):  
Loemongga Oktaria Sihombing ◽  
◽  
Hannie Hannie ◽  
Budi Arif Dermawan ◽  
◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Business World ◽  
Business People ◽  
Main Challenge ◽  
Customer Review ◽  
Bayes Algorithm

Gaining customer satisfaction and trust has become the main challenge in achieving success in the business world. Business people need to identify problems that arise from reviews given by customers. However, reading and classifying each review takes a long time and is considered ineffective. To overcome this, this study aims to analyze the customer sentiment of shopee products using the nave Bayes classifier algorithm. The data used in this study is a customer review of the Xiaomi Redmi Note 9 products which are sold on the Shopee Indonesia website. Customer review data is collected by applying the Web Scraping technique. The algorithm used in this study is the Naïve Bayes Classifier which is known to be popular and effective in classifying data. This study also applies the Knowledge Discovery in Text (KDT) methodology to extract information from text data. The results of the classification using the Naïve Bayes algorithm found an accuracy value of 85%. This study proves that by applying sentiment analysis techniques, business people are able to find out the opinions of customers as an evaluation material that needs to be done to optimize the products and services provided.

scholarly journals Detection of Congestive Heart Failure using Naive Bayes Classifier

2020 ◽  
Vol 9 (3) ◽  
pp. 4154-4159
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Lower Cost ◽  
Naive Bayes ◽  
Normal Subjects ◽  
Naïve Bayes ◽  
P Value ◽  
Bayes Classifier ◽  
Modern Life ◽  
Bayes Algorithm

Congestive heart failure (CHF) is gradually becoming more prevalent due to the stressed lifestyles in modern life. Accurate detection with lower computational complexity and lower cost of diagnosis is a challenge to the researchers in this domain. In this work, I have proposed an approach using naive Bayes algorithm with a lesser number of significantly discriminating features for differentiating the CHF subjects from the normal subjects. The small size of feature sets enhances the computational efficiency and the choice of strong features improves the accuracy. The features are chosen on the basis of p-value of the 2-sample t-test performed between the two types of subjects. Using the p-value, 6 features are selected to train, validate and test the classifier. Publicly available benchmark PhysioNet datasets for congestive heart failure patients and normal subjects are used to carry out the experimentation. This approach is able to provide 100% classification accuracy as well as sensitivity and specificity of 100% in identifying CHF patients employing Gaussian naive Bayes algorithm.

Implementing Naive Bayes Algorithm for Detecting Spam Emails on Datasets

2020 ◽  
Author(s):  
ARGHA GHOSH ◽  
A. SENTHILRAJAN
Keyword(s):  
Naive Bayes ◽  
Research Work ◽  
Bayes Theorem ◽  
Naïve Bayes ◽  
Data Sets ◽  
Bayes Classifier ◽  
Ve Bayes ◽  
Target User ◽  
Multiple Data Sets ◽  
Bayes Algorithm

Email is the most common as well as the fastest medium for communicating around the globe. But, presently every day we used to get lots of junk emails in the name of &ldquo;spam&rdquo;. This &ldquo;spam&rdquo; emails mainly used to contain two types of content, those are content like an advertisement, offers and, criminal activity content like a phishing website link, malware, trojan, etc. Those advertisements, offer types of spam or junk emails known as Unsolicited Commercial Emails and, those emails contain phishing website link, malware, trojan used to known as Unsolicited Bulk Emails. Whoever used to send spam emails, they are known as Spammers. Spammers mainly used to get the email address of target user from the websites, junk sites, browsers add on, etc. Naive Bayes algorithm is a probabilistic machine learning algorithm that mainly well-known for classifying spam emails. Naive Bayes algorithm mainly originated from Bayes Theorem. Bayes Theorem mainly used in conditional probability for elaborates the probability of an event in terms of when the probability of other event is true. In this research work, we have been performing Feature Extraction in terms of email characteristics and behavior. In this paper, we have been proposed a detection approach for classifying spam emails using Na&iuml;ve Bayes classifier. In this research work, we have been used multiple email data-sets for implementing Na&iuml;ve Bayes classifier. Those data sets are Spam Corpus, Spambase. Based on the results of WEKA (Waikato Environment for Knowledge Analysis) tool, we have been performing Experimental analysis in terms of measuring the performance of Na&iuml;ve Bayes classifier using parameters like Accuracy, Recall, Precision, F-measure. Based on correctly classified instances of emails and incorrectly classified instances of emails, lastly comparing the performance of Na&iuml;ve Bayes classifier in multiple data sets.

Cyberbully Detection Using Term Weighting Scheme and Naïve Bayes Classifier

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rafeena Mohamad Rabii ◽  
Maheyzah Md Siraj
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Term Weighting ◽  
Weighting Schemes ◽  
Bayes Algorithm

The internet especially social media has been a major platform where people interact with each other. We are able to interact with each other regardless of time and place because of the advancement of technology. Unfortunately, not all of the interaction that goes on are good or positive. One of the negative interaction that can happen online is cyberbullying which has rapidly increase throughout the years, whether it be through social media, emails or texting. Therefore, it is important to prevent cyberbullying from occurring which is why this research is done. Detection the presence of cyberbullying is one if the main issue in avoiding it from happening. Cyberbullying detection can be challenging because the many languages used in the world, most of the time slangs and informal languages are used and special characters like emoji are also used during online conversation. The aim of this research is to detect the presence of text cyberbullying from online post. Two term weighting schemes and two classification algorithms are compared in this research. The weighting schemes used namely Entropy and Term Frequency -  Inverse Document Frequency (TF-IDF) for feature selection and Naïve Bayes algorithm is used and compared with Support Vector Machine (SVM) algorithm. As a result, it shows that Naïve Bayes classifier yields a better accuracy when used with TF-IDF which is 97.60%. Hopefully this research is able give other researchers an insight, particularly to those who are interested in a similar area.

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