Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine

2021 ◽  
Vol 5 (2) ◽  
pp. 153-163
Author(s):  
Herlawati Herlawati ◽  
Rahmadya Trias Handayanto ◽  
Prima Dina Atika ◽  
Fata Nidaul Khasanah ◽  
Ajif Yunizar Pratama Yusuf ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Data Retrieval ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Method ◽  
Computational Tools

 Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework.   Keywords:

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

scholarly journals Aspect Based Sentiment Analysis of Nepali Text Using Support Vector Machine and Naive Bayes

2020 ◽  
Vol 2 (1) ◽  
pp. 22-29
Author(s):  
Sujan Tamrakar ◽  
Bal Krishna Bal ◽  
Rajendra Bahadur Thapa
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Svm Classifier ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Document Frequency ◽  
Classifier Algorithms

Aspect-based Sentiment Analysis assists in understanding the opinion of the associated entities helping for a better quality of a service or a product. A model is developed to detect the aspect-based sentiment in Nepali text using Machine Learning (ML) classifier algorithms namely Support Vector Machine (SVM) and Naïve Bayes (NB). The system collects Nepali text data from various websites and Part of Speech (POS) tagging is applied to extract the desired features of aspect and sentiment. Manual labeling is done for each sentence to identify the sentiment of the sentence. Term Frequency – Inverse Document Frequency (TF-IDF) is applied to compute the importance of the words. The feature vectors thus produced are then applied to the Classifier algorithms to predict and classify the sentence. The accuracy obtained by the SVM classifier is 76.8% whereas Bernoulli NB is 77.5%.

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 Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter

2019 ◽  
Vol 5 (2) ◽  
pp. 100
Author(s):  
Debby Alita ◽  
Sigit Priyanta ◽  
Nur Rokhman
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Process

Background: Indonesia is an active Twitter user that is the largest ranked in the world. Tweets written by Twitter users vary, from tweets containing positive to negative responses. This agreement will be utilized by the parties concerned for evaluation.Objective: On public comments there are emoticons and sarcasm which have an influence on the process of sentiment analysis. Emoticons are considered to make it easier for someone to express their feelings but not a few are also other opinion researchers, namely by ignoring emoticons, the reason being that it can interfere with the sentiment analysis process, while sarcasm is considered to be produced from the results of the sarcasm sentiment analysis in it.Methods: The emoticon and no emoticon categories will be tested with the same testing data using classification method are Naïve Bayes Classifier and Support Vector Machine. Sarcasm data will be proposed using the Random Forest Classifier, Naïve Bayes Classifier and Support Vector Machine method.Results: The use of emoticon with sarcasm detection can increase the accuracy value in the sentiment analysis process using Naïve Bayes Classifier method.Conclusion: Based on the results, the amount of data greatly affects the value of accuracy. The use of emoticons is excellent in the sentiment analysis process. The detection of superior sarcasm only by using the Naïve Bayes Classifier method due to differences in the amount of sarcasm data and not sarcasm in the research process.Keywords:  Emoticon, Naïve Bayes Classifier, Random Forest Classifier, Sarcasm, Support Vector Machine

scholarly journals Aplikasi Mobile untuk Analisis Sentimen pada Google Play

2015 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Lutfi Budi Ilmawan ◽  
Edi Winarko
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Mobile Device ◽  
Naive Bayes ◽  
Limited Resource ◽  
Naïve Bayes ◽  
Support Vector ◽  
Svm Classifier ◽  
Client Server ◽  
Google Play

AbstrakGoogle dalam application store-nya, Google Play, saat ini telah menyediakan sekitar 1.200.000 aplikasi mobile. Dengan sejumlah aplikasi tersebut membuat pengguna memiliki banyak pilihan. Selain itu, pengembang aplikasi mengalami kesulitan dalam mencari tahu bagaimana meningkatkan kinerja aplikasinya. Dengan adanya permasalahan tersebut, maka dibutuhkan sebuah aplikasi analisis sentimen yang dapat mengolah sejumlah komentar untuk memperoleh informasi.Sistem yang dibangun memiliki tujuan untuk menentukan polaritas sentimen dari ulasan tekstual aplikasi pada Google Play yang dilakukan dari perangkat mobile. Perangkat mobile memiliki portabilitas yang tinggi dan sebagian dari perangkat tersebut memiliki resource yang terbatas. Hal tersebut diatasi dengan menggunakan arsitektur sistem berbasis client server, di mana server melakukan tugas-tugas yang berat sementara client-nya adalah perangkat mobile yang hanya mengerjakan tugas yang ringan. Dengan solusi tersebut maka Analisis sentimen dapat diaplikasikan pada mobile environment.Adapun metode klasifikasi yang digunakan adalah Naïve Bayes untuk aplikasi yang dikembangkan dan Support Vector Machine Linier sebagai pembanding. Nilai akurasi dari Naïve Bayes classifier dari aplikasi yang dibangun sebesar 83,87% lebih rendah jika dibandingkan dengan nilai akurasi dari SVM Linier classifier sebesar 89,49%. Adapun penggunaan semantic handling untuk mengatasi sinonim kata dapat mengurangi akurasi classifier. Kata kunci— analisis sentimen, google play, klasifikasi, naïve bayes, support vector machine AbstractGoogle's Google Play now providing approximately 1.200.000 mobile applications. With these number of applications, it makes the users have many options. In addition, application developers have difficulties in figuring out how to improve their application performance. Because of these problems, it is necessary to make a sentiment analysis applications that can process review comments to get valuable information.The purpose of this system is determining the polarity of sentiments from applications’s textual reviews on Google Play that can be performed on mobile devices. The mobile device has high portability and the majority of these devices have limited resource. That problem can be solved by using a client server based system architecture, where the server performs training and classification tasks while clients is a mobile device that perform some of sentiment analysis task. With this solution, the sentiment analysis can be applied to the mobile environment.The classification method that used are Naive Bayes for developed application and Linear Support Vector Machine that is used for comparing. Naïve Bayes classifier’s accuracy is 83.87%. The result is lower than the accuracy value of Linear SVM classifier that reach 89.49%. The use of semantic handling can reduce the accuracy of the classifier. Keywords—sentiment analysis, google play, classification, naïve bayes, support vector machine

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 Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

2020 ◽  
Vol 1641 ◽  
pp. 012085
Author(s):  
Dwi Andini Putri ◽  
Dinar Ajeng Kristiyanti ◽  
Elly Indrayuni ◽  
Acmad Nurhadi ◽  
Denda Rinaldi Hadinata
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Selection For ◽  
Bayes Algorithm

scholarly journals SENTIMEN ANALISIS OPERASI TANGKAP TANGAN KPK MENURUT MASYARAKAT MENGGUNAKAN ALGORITMA SUPPORT VECHTOR MACHINE, NAÏVE BAYES, BERBASIS PARTICLE SWARM OPTIMIZITION

Faktor Exacta ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 230
Author(s):  
Hernawati Hernawati ◽  
Windu Gata Kedua
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Bayes Method ◽  
Machine Method ◽  
Swarm Optimization ◽  
Support Vector Machine Method

<p><em>It is known from various public sentiments conveyed through comments on social media twitter against the capture operations carried out by the corruption eradication commission (KPK) that currently it does not meet the expectations of the community, where officials who are only officials have small corruption rates, not corruption As for the classification algorithms that have strong accuracy at this time are Support Vector Machine and Naïve Bayes algorithms, calculation of Support Vector Machine method for tweet data from 78 positive tweet data and 78 negative tweet data, resulting in an accuracy of 80.77% and AUC 0.867. Whereas the results of accuracy with the Naïve Bayes method are 76.92% and AUC 0.729. Having a difference in accuracy of 3.3%, and after optimizing with the Operator Vector Machine (PSO) weight Particle Swarm Optimization the accuracy is 83.79% and AUC 0.910, while for Naïve Bayes (PSO) produces an accuracy of 80.13% and AUC 0.771 Has a difference in accuracy of 3.6%.</em></p><p><em><br /></em></p><p><em>Diketahui dari berbagai sentimen masyarakat yang disampaikan melalui komentar di media sosial <em>twiter </em>terhadap operasi tangkap tangan yang dilakukan oleh Komisi Pemberantasan Korupsi (KPK) nyatanya saat ini belum memenuhi harapan masyarakat, dimana pejabat yang di ott hanya pejabat yang mempunyai angka korupsi kecil, bukan korupsi yang besar adapun algoritma klasifikasi yang kuat akurasinya saat ini adalah algoritma<em> Support Vector Machine </em>untuk data <em>tweet</em> dari 78 data tweet positif dan 78 data tweet negatif, menghasilkan akurasi sebesar 80.77% dan AUC 0.867. Sedangkan hasil akurasi dengan metode<em> Naïve Bayes</em> adalah 76.92% dan AUC 0.729. Memiliki selisih akurasi sebesar 3.3%, dan setelah di optimalisasi dengan oprator <em>Weight Partical Swarm Optimization</em> untuk <em>Support Vector Machine (PSO)</em> menghasilkan akurasi 83.79% dan AUC 0.910, sedangkan untuk <em>Naïve Bayes</em> (PSO) menghasilkan akurasi sebesar 80.13% dan AUC 0.771 memiliki selisih akurasi sebesar 3.6%.</em></p>

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