scholarly journals Comparison of Naïve Bayes, Support Vector Machine, Decision Trees and Random Forest on Sentiment Analysis

2019 ◽  
Author(s):  
Márcio Guia ◽  
Rodrigo Silva ◽  
Jorge Bernardino
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Decision Trees ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector

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 ALGORITMOS DE APRENDIZAGEM DE MÁQUINA E VARIÁVEIS DE SENSORIAMENTO REMOTO PARA O MAPEAMENTO DA CAFEICULTURA

2016 ◽  
Vol 22 (4) ◽  
pp. 751-773 ◽  
Author(s):  
Carolina Gusmão Souza ◽  
Luis Carvalho ◽  
Polyanne Aguiar ◽  
Tássia Borges Arantes
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector

A cafeicultura é uma das principais culturas agrícolas do Brasil e realizar o mapeamento e monitoramento desta cultura é fundamental para conhecer sua distribuição espacial. Porém, mapear estas áreas utilizando imagens de Sensoriamento Remoto não é uma tarefa fácil. Sendo assim, este trabalho foi realizado com o objetivo de comparar o uso de diferentes variáveis e algoritmos de classificação para o mapeamento de áreas cafeeiras. O trabalho foi desenvolvido em três áreas diferentes, que são bastante significativas na produção de café. Foram utilizados 5 algoritmos de aprendizagem de máquinas e 7 combinações de variáveis: espectrais, texturais e geométricas, associadas ao processo de classificação. Um total de 105 classificações foram realizadas, 35 classificações para cada uma das áreas. As classificações que não usaram variáveis espectrais não resultaram em bons índices de acurácia. Nas três áreas, o algoritmo que apresentou as melhores acurácias foi o Support vector machine, com acurácia global de 85,33% em Araguari, 87% em Carmo de Minas e 88,33% em Três Pontas. Os piores resultados foram encontrados com o algoritmo Random Forest em Araguari, com acurácia global de 76,66% e com o Naive Bayes em Carmo de Minas e Três Pontas, com 76% e 82% de acerto. Nas três áreas, variáveis texturais, quando associadas às espectrais, melhoraram a acurácia da classificação. O SVM apresentou o melhor desempenho para as três áreas

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 Perbandingan Klassifikasi SMS Berbasis Support Vector Machine, Naive Bayes Classifier, Random Forest dan Bagging Classifier

2021 ◽  
Vol 10 (3) ◽  
pp. 432-437
Author(s):  
Devi Irawan ◽  
Eza Budi Perkasa ◽  
Yurindra Yurindra ◽  
Delpiah Wahyuningsih ◽  
Ellya Helmud
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Short Message ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Message Service

Short message service (SMS) adalah salah satu media komunikasi yang penting untuk mendukung kecepatan pengunaan ponsel oleh pengguna. Sistem hibrid klasifikasi SMS digunakan untuk mendeteksi sms yang dianggap sampah dan benar. Dalam penelitian ini yang diperlukan adalah mengumpulan dataset SMS, pemilihan fitur, prapemrosesan, pembuatan vektor, melakukan penyaringan dan pembaharuan sistem. Dua jenis klasifikasi SMS pada ponsel saat ini ada yang terdaftar sebagai daftar hitam (ditolak) dan daftar putih (diterima). Penelitian ini menggunakan beberapa algoritma seperti support vector machine, Naïve Bayes classifier, Random Forest dan Bagging Classifier. Tujuan dari penelitian ini adalah untuk menyelesaikan semua masalah SMS yang teridentifikasi spam yang banyak terjadi pada saat ini sehingga dapat memberikan masukan dalam perbandingan metode yang mampu menyaring dan memisahkan sms spam dan sms non spam.  Pada penelitian ini menghasilkan bahwa Bagging classifier algorithm ini mendapatkan ferformance score tertinggi dari algoritma yang lain yang dapat dipergunakan sebagai sarana untuk memfiltrasi SMS yang masuk ke dalam inbox pengguna dan Bagging classifier algorithm dapat memberikan hasil filtrasi yang akurat untuk menyaring SMS yang masuk.

scholarly journals Εξόρυξη γνώσης από αρχεία μεγάλου όγκου δεδομένων υγείας -Big Data- με χρήση υπολογιστικών αλγορίθμων ανάλυσης - Health Analytics

2021 ◽  
Author(s):  
Ιωάννης Μήνου
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Random Forest ◽  
Cross Validation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Η μεγαλύτερη πρόκληση των σύγχρονων υπολογιστικών συστημάτων είναι αναμφισβήτητα η αποδοτική αποθήκευση και ανάκτηση πολύ μεγάλου όγκου δεδομένων. Η ανάγκη αυτή έκανε την εμφάνισή της τα τελευταία χρόνια λόγω της έκρηξης δεδομένων που παρατηρείται στο διαδίκτυο και αποκτά ολοένα και μεγαλύτερη σημασία λόγω του πολύ μεγάλου εύρους πληροφοριών που μπορούμε να αντλήσουμε. Ο τομέας της υγειονομικής περίθαλψης και των ιατρικών δεδομένων είναι συνεχώς και ταχέως εξελισσόμενος. Η αξιοποίηση των Big Data στο χώρο της υγείας προσφέρει πολύτιμη πληροφόρηση καθώς παρουσιάζουν απεριόριστες δυνατότητες για αποτελεσματική αποθήκευση, επεξεργασία, sql queries και ανάλυση ιατρικών δεδομένων.Σκοπός της παρούσας διατριβής είναι η μελέτη τεχνικών εξόρυξης γνώσης για δεδομένα μεγάλου όγκου, που αφορούν το πεδίο της Υγείας. Παράλληλα σκοπός της έρευνας είναι η μελέτη στατιστικών και υπολογιστικών αλγορίθμων ανάλυσης μεγάλου όγκου δεδομένων υγείας που έχουν ως αποτέλεσμα την παραγωγή νέας γνώσης καθώς και την εξαγωγή στατιστικά σημαντικής πληροφορίας για τους επαγγελματίες υγείας. Τέλος, η παρούσα διατριβή διερευνά τις γνώσεις των επιστημόνων της Πληροφορικής Υγείας και των επαγγελματιών υγείας σχετικά με τα Big Data.Στην παρούσα διδακτορική διατριβή έγινε βιβλιογραφική ανασκόπηση της έννοιας των Big Data. Η ανασκόπηση αυτή περιλαμβάνει τον ορισμό των Big Data ,τα χαρακτηριστικά τους, τα πλεονεκτήματα και τα μειονεκτήματά τους στο χώρο της υγείας. Στη συνέχεια γίνεται αναφορά στην υλοποίηση και στους μηχανισμούς αποθήκευσης των Big Data. Επιπλέον γίνεται αναφορά στα συστήματα ανάλυσης και επεξεργασίας μεγάλου όγκου δεδομένων, στις γλώσσες προγραμματισμού για Big Data, στην εξόρυξη γνώσης δεδομένων στο χώρο της υγείας. Ακόμη γίνεται αναφορά στη χρήση των Big Data στην Ευρώπη και στον κόσμο. Τέλος παρουσιάζονται οι βασικές αρχές του GDPR καθώς και το πώς σχετίζεται με τα Big Data στο χώρο της υγείας. Επίσης διεξήχθησαν δύο εμπειρικές μελέτες.Η πρώτη μελέτη είχε σαν στόχο την καταγραφή της άποψης των επιστημόνων της Πληροφορικής Υγείας σχετικά με την τεχνολογία των Big Data. Η συλλογή των δεδομένων έγινε με χρήση ερωτηματολογίου. Η στατιστική ανάλυση έδειξε τη θετική ανταπόκριση του δείγματος σχετικά με την τεχνολογία των Big Data.Η δεύτερη μελέτη είχε σαν στόχο την καταγραφή της άποψης των Επαγγελματιών Υγείας σχετικά με την τεχνολογία των Big Data. Η συλλογή των δεδομένων έγινε με χρήση ερωτηματολογίου. Η στατιστική ανάλυση δεν έδωσε επαρκείς απαντήσεις καθώς οι ερωτηθέντες έδειξαν θετική στάση απέναντι στα Big Data ενώ απάντησαν ότι δεν γνωρίζουν πολλά για τη συγκεκριμένη τεχνολογία.Το τελευταίο κομμάτι της διατριβής περιλαμβάνει την ανάπτυξη μεθόδων πρόβλεψης για την δυνατότητα διάγνωσης των ασθενών με καρδιαγγειακά νοσήματα. Οι μέθοδοι πρόβλεψης που χρησιμοποιήθηκαν είναι: Λογιστική Παλινδρόμηση, Naive Bayes Classifier, Δένδρα αποφάσεων, Αλγόριθμος Κ κοντινότερων γειτόνων, Αλγόριθμος SVM (Support Vector Machine) και Random Forest. Η ανάπτυξη περιλάμβανε όλα τα στάδια προεπεξεργασίας των δεδομένων ενώ χρησιμοποιήθηκαν συγκεκριμένες μετρικές για τη μέτρηση της απόδοσης των κατηγοριοποιητών. Τέλος έγιναν βελτιώσεις της απόδοσης των κατηγοριοποιητών χρησιμοποιώντας διασταυρωτική επαλήθευση με την μέθοδο cross-validation ενώ επιλύθηκε και το πρόβλημα της ανισορροπίας των κλάσεων χρησιμοποιώντας τη μέθοδο SMOTE.

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.

Prediction of Breast Cancer Using Machine Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 901-908
Author(s):  
Somil Jain ◽  
Puneet Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Breast Cancer Dataset

Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

Sign in / Sign up

Export Citation Format

Share Document