scholarly journals Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Abi Rafdi ◽  
Herman Mawengkang Herman ◽  
Syahril Efendi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Bayes Algorithm

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.

scholarly journals Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

2020 ◽  
Vol 8 (2) ◽  
pp. 91-100
Author(s):  
Muhamad Azhar ◽  
Noor Hafidz ◽  
Biktra Rudianto ◽  
Windu Gata
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Cross Validation ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Swarm Optimization

Abstract   Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study.   Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.

scholarly journals Sentiment Analysis Analysis of E-Wallet Sentiments on Google Play Using the Naive Bayes Algorithm Based on Particle Swarm Optimization

2019 ◽  
Vol 3 (3) ◽  
pp. 377-382
Author(s):  
Suwanda Aditya Aaputra ◽  
Didi Rosiyadi ◽  
Windu Gata ◽  
Syepry Maulana Husain
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Special Method ◽  
Swarm Optimization ◽  
The Public ◽  
Bayes Algorithm ◽  
Google Play

Increasingly sophisticated technology brings various conveniences both in transportation, information, education to the convenience of transactions in shopping, such as the development of E-wallet can now be easily done using a smartphone. From a number of e-wallet products, researchers took a case study, which is OVO product, which is currently being discussed by many groups, especially in the capital of Jakarta today. Customers or clients who are not satisfied with the services or products offered by a company will usually write their complaints on social media or reviews on Google play. However, monitoring and organizing opinions from the public is also not easy. For this reason, we need a special method or technique that is able to categorize these reviews automatically, whether positive or negative. The algorithm used in this study is Naive Bayes Classifier (NB), with the optimization of the use of Particle Swarm Optimization Feature Selection (FS). The results of cross validation NB without FS are 82.30% for accuracy and 0.780 for AUC. Whereas for NB with FS is 83.60% for accuracy and 0.801 for AUC. Very significant improvement with the use of Feature Selection (FS) Particle Swarm Optimization.  

Implementasi Particle Swarm Optimization (PSO) Pada Analysis Sentiment Review Aplikasi Halodoc Menggunakan Algoritma Naïve Bayes

2021 ◽  
Vol 7 (1) ◽  
pp. 17-23
Author(s):  
Nuzuliarini Nuris ◽  
Eka Rini Yulia ◽  
Kusmayanti Solecha
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Roc Curves ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Classification Evaluation ◽  
Bayes Algorithm ◽  
Increase In Accuracy

Health is very important for humans, if you experience symptoms or feel pain, it is appropriate for us to have a health check and go to a hospital or clinic, but if it is not possible to leave the house, an online health consultation application is considered to be helpful. But before you can use and take advantage of these applications, it is necessary to know reviews from consumers based on positive opinions and negative opinions. This study applies the Naive Bayes algorithm to perform text classification and selects the particle swarm optimazation selection feature to support the increased accuracy obtained. Classification evaluation and validation are performed using confusion matrix and ROC curves. The results showed an increase in accuracy previously 88.50% and AUC 0.535, increased to 90.50% and AUC 0.525. It can be concluded that the selection of the particle swarm optimazation feature has succeeded in increasing the accuracy.Keywords: selection features, naïve bayes, particle swarm optimization.

scholarly journals SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION

2020 ◽  
Vol 2 (3) ◽  
pp. 169-178
Author(s):  
Zulia Imami Alfianti ◽  
Deni Gunawan ◽  
Ahmad Fikri Amin
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advantages And Disadvantages ◽  
Improve Accuracy

Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions

scholarly journals ANALISA NASABAH POTENSIAL TABUNGAN DEPOSITO BERJANGKA MENGGUNAKAN TEKNIK KLASIFIKASI DATA MINING

2019 ◽  
Vol 5 (2) ◽  
pp. 105-112
Author(s):  
Candra Agustina
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Financial Institution ◽  
Unified Modeling Language ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Time Deposit ◽  
Bayes Algorithm ◽  
Potential Customers

Time deposits are a product of a financial institution, which is currently increasing. The main target of this time deposit marketing is the old customers of the Bank. To increase the effectiveness of marketing customers are grouped into potential and non-potential customers. This means that potential customers have a greater chance to open a time deposit account. Customer data is taken from the UCI repository, originating from Banks in Portugal. Data is processed with rapidminer software using the Decision Tree method with Particle Swarm Optimization, Naïve Bayes with Particle Swarm Optimization and finally processed using Neural Network with Particle Swarm Optimization. Data processing results were compared and showed that the Naïve Bayes Algorithm with Particle Swarm Optimization had the highest accuracy of 97.04%. Therefore an application designed based on Naive Bayes with Particle Swarm Optimization. From the original attribute consisting of 20, only 9 attributes can be used so that the level of accuracy is high. Attributes used have values ​​more than 0.500, while those that have these values ​​are omitted. The design was created using the Unified Modeling Language (UML) and Visual Basic 6.0 to create an User Interface.

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 EMPLOYEE TURNOVER CLASSIFICATION USING PSO-BASED NAÏVE BAYES AND NAÏVE BAYES ALGORITHM IN PT. MASTERSYSTEM INFOTAMA

2021 ◽  
Vol 3 (3) ◽  
pp. 233-240
Author(s):  
Endang Sri Palupi
Keyword(s):  
Particle Swarm Optimization ◽  
Employee Engagement ◽  
Employee Turnover ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Business World ◽  
New Employees ◽  
Bayes Algorithm

Turnover occurs because many employees leave and new employees enter, so the turnover in and out of employees is quite high, therefore turnover can be controlled with a strategy to increase employee engagement. PT. Mastersystem Infotama is a System Integrator company or better known as a fairly large IT company with a total of approximately 600 employees. Turnover is high enough to make some divisions lack human resources, and the human capital management division is quite difficult to recruit employees to find candidates with various criteria that must be available in a short time. Competition in the IT world is quite tight both within companies and employees with good experience and abilities. Especially the sales department that holds a database of potential customers, and the engineer section that already has a certificate of expertise that is widely used in the IT business world. Therefore, it is necessary to classify what factors make employee turnover high by using the Naïve Bayes and Naïve Bayes algorithms based on Particle Swarm Optimization, so that they can be used as material for internal evaluation to increase employee engagement. The results of this study, classification using the Naïve Bayes algorithm, has an accuracy of 79.17%, while the classification using the Naïve Bayes algorithm based on Particle Swarm Optimization is 94.17%.

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