scholarly journals Veil and Hijab: Twitter Sentiment Analysis Perspective

2020 ◽  
Vol 9 (1) ◽  
pp. 52
Author(s):  
Lusiana Lestari ◽  
M Didik R Wahyudi ◽  
Usfita Kiftiyani
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Accuracy Score ◽  
Base Function ◽  
Rbf Kernel ◽  
The Veil

Controversies about veil and hijab are often occur in society. Especially in today’s digital era, public opinion expressed through social media can greatly influence the others opinions, regardless of whether it is positive or negative. Therefore, this research was aiming to conduct an approach through analysis sentiment of public opinion about the veil and hijab to know how much accurate the sentiment analysis predict the positive, negative, or other sentiments with using Twitter data as the research object. The algorithm used in this study is Support Vector Machine (SVM) because of its fairly good classification model though it trained using small set of data. The SVM on this research was combined with Radial Base Function (RBF) kernel because of its numerical difficulties that are fewer than linear and polynomial kernel and also because this research doesn’t have a large feature.  The amount of data used is 3556 tweets data. Tweets data, which is numbered 1056, is classified manually for the learning process. The remaining 2500 data will be classified automatically with the classifier model that has been created. A total of 1056 tweets data that have been classified manually is separated into training and testing data with a ratio of 8: 2. The result of the sentiment analysis process using Support Vector Machine algorithm RBF kernel with C=1 and γ=1  has an accuracy score of 73.6% with precision to negative opinions are 62%, positive opinions are 83%, neutral opinions reach 53% and irrelevant opinions that talk about hijab and veil reach 98%. It shows that sentiment analysis can be used for predicting the negative, positive or other sentiments of a sentence based on a certain topic, in this case veil and hijab.

scholarly journals High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

2018 ◽  
Vol 3 (2) ◽  
pp. 194
Author(s):  
Lailil Muflikhah ◽  
Dimas Joko Haryanto
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
High Performance ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Algorithm ◽  
Rbf Kernel ◽  
C Value ◽  
Parameter Values

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.

scholarly journals Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 667
Author(s):  
Wismaji Sadewo ◽  
Zuherman Rustam ◽  
Hamidah Hamidah ◽  
Alifah Roudhoh Chusmarsyah
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Support Vector Machine ◽  
Early Detection ◽  
Feature Space ◽  
Kernel Functions ◽  
Twin Support Vector Machine ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

scholarly journals PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

2021 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Shafira Shalehanny ◽  
Agung Triayudi ◽  
Endah Tri Esti Handayani
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Food Service ◽  
Support Vector ◽  
Accuracy Score ◽  
Online Business ◽  
Data Source ◽  
Sentiment Score ◽  
Processing Language

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

scholarly journals Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model

2021 ◽  
Vol 3 (1) ◽  
pp. 9-14
Author(s):  
Tsehay Admassu Assegie
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Liver Disease ◽  
Classification Model ◽  
Support Vector ◽  
Disease Prediction ◽  
Accuracy Score ◽  
Learning Models ◽  
Accuracy And Precision ◽  
Machine Learning Models

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.  

scholarly journals ANALISIS SENTIMEN GOJEK PADA MEDIA SOSIAL TWITTER DENGAN KLASIFIKASI SUPPORT VECTOR MACHINE (SVM

2020 ◽  
Vol 9 (3) ◽  
pp. 376-390
Author(s):  
Nur Fitriyah ◽  
Budi Warsito ◽  
Di Asih I Maruddani
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Classification Model ◽  
Support Vector ◽  
Test Results ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Rbf Kernel ◽  
Negative Sentiment ◽  
Fold Cross Validation

Appearance of PT Aplikasi Karya Anak Bangsa or as known as Gojek since 2015 give a convenience facility to people in Indonesia especially in daily activities. Sentiment analysis on Twitter social media can be the option to see how Gojek users respond to the services that have been provided. The response was classified into positive sentiment and negative sentiment using Support Vector Machine method with model evaluation 10-fold cross validation. The kernel used is the linear kernel and the RBF kernel. Data labeling can be done with manually and sentiment scoring. The test results showed that the RBF kernel gets overall accuracy and the highest kappa accuracy on manual data labeling and sentiment scoring. On manual data labeling, the overall accuracy is 79.19% and kappa accuracy is 16.52%. While the labeling of data with sentiment scoring obtained overall accuracy of 79.19% and kappa accuracy of 21%. The greater overall accuracy value and kappa accuracy obtained, the better performance of the classification model. Keywords: Gojek, Twitter, Support Vector Machine, overall accuracy, kappa accuracy

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

scholarly journals Support Vector Machine Based Feature Extraction For Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Face Recognition ◽  
Selection Process ◽  
Recognition System ◽  
Classification Model ◽  
Support Vector ◽  
Gender Classification ◽  
Accuracy Score ◽  
Lasso Regression

Abstract The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. Facial record images are immobile an difficult assignment for biometric authentication systems due to various types of characteristics are dimensions, pose, expressions, illustrations and age etc. In facial and other united images includes different objects classifications. In this research article, a minimum distance trainer for feature selection by accessing SVM feature optimization process. For feature selection process SVM (support vector machine) was considered for improving its feature interpretability and computational efficiency., then LASSO classifier applied to perform object recognition and gender classification. Original face image database used for the gender classification. This approach was implemented with dual classification model (1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classification.

scholarly journals IDENTIFIKASI KEBUTUHAN DASAR DI TEMPAT EVAKUASI SEMENTARA PASCA ERUPSI MERAPI DENGAN SENTIMENT ANALISIS DAN SUPPORT VECTOR MACHINE

Telematika ◽  
2018 ◽  
Vol 15 (1) ◽  
pp. 77
Author(s):  
Resky Rayvano Moningka ◽  
Djoko Budiyanto Setyohadi ◽  
Khaerunnisa Khaerunnisa ◽  
Pranowo Pranowo
Keyword(s):  
Support Vector Machine ◽  
Public Opinion ◽  
Maximum Entropy ◽  
Disaster Management ◽  
Cross Validation ◽  
Basic Needs ◽  
Classification Model ◽  
Support Vector ◽  
Twitter Data ◽  
The Impact

AbstractMount Merapi Eruption in 2010 was the biggest after 1872. The impact of this eruption was felt by people who lived around the areas which were affected by this Merapi Eruption. Thus, disaster management was done. One of the disaster management was the fulfillment of basic needs. This research aims to collect public opinion against the fulfillment of basic needs in the shelters after Merapi Eruption based on Twitter data. The algorithm which is used in this research is Support Vector Machine to develop classification model over the data that has been collected. The expected result from this study is to know the basic needs in a shelter. The accuracy gained by performing Cross Validation for 10 folds from Support Vector Machine is 87.96% and Maximum Entropy is 87.45%. Keywords: twitter, sentiment analisis, merapi eruption, support vector machine AbstrakErupsi Gunung Merapi 2010 merupakan yang terbesar setelah tahun 1872. Dampak dari Erupsi Gunung Merapi dirasakan oleh masyarakat yang tinggal di daerah terdampak Erupsi Merapi. Oleh sebab itu dilakukan penanggulangan Bencana. salah satu penanggulangan bencana adalah pemenuhan kebutuhan dasar. Penelitian ini bertujuan untuk mengumpulkan opini publik terhadap pemenuhan kebutuhan dasar di tempat pengungsian pasca erupsi merapi berdasarkan data Twitter. Algoritma yang digunakan dalam penelitian ini adalah Support Vector Machine untuk membangun model klasifikasi atas data yang sudah dikumpulkan.   Hasil yang diharapkan dari penelitian ini adalah mengetahui kebutuhan dasar dari suatu tempat pengungsian. Akurasi yang didapatkan dengan melakukan Cross Validation sebanyak 10 fold dari model klasifikasi Support Vector Machine87,96% dan Maximum Entropy 87,45 Kata Kunci: twitter, analisis sentimen, erupsi merapi, support vector machine

scholarly journals Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM)

2020 ◽  
Vol 4 (3) ◽  
pp. 650
Author(s):  
Rian Tineges ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Support Vector ◽  
Internet Service ◽  
Active User ◽  
The Social ◽  
Twitter Users ◽  
Use Of The Internet

In the year 2018, 18.9% of the population in Indonesia mentioned that the main reason for their use of the Internet is social media. One of the social media with an active user of 6.43 million users is Twitter. Based on the surge of information published via Twitter, it is possible that such information may contain the user's opinions on an object, such objects may be events around the community such as a product or service. This makes the company use Twitter as a medium to disseminate information. An example is an Internet Service Provider (ISP) such as Indihome. Through Twitter, users can discuss each other's complaints or satisfaction with Indihome's services. It takes a method of sentiment analysis to understand whether the textual data includes negative opinions or positive opinions. Thus, the authors use the Support Vector Machine (SVM) method in sentiment analysis on the opinions of the Indihome service user on Twitter, with the aim of obtaining a sentiment classification model using SVM, and to know how much accuracy the SVM method generates, which is applied to sentiment analysis, and to see how satisfied the Indihome service users are based on Twitter. After testing with SVM method The result is accuracy 87%, precision 86%, recall 95%, error rate 13%, and F1-score 90%

scholarly journals Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 86-96
Author(s):  
Ricky Risnantoyo ◽  
Arifin Nugroho ◽  
Kresna Mandara
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Public ◽  
Machine Learning Classification ◽  
Corona Virus

Corona virus outbreaks that occur in almost all countries in the world have an impact not only in the health sector, but also in other sectors such as tourism, finance, transportation, etc. This raises a variety of sentiments from the public with the emergence of corona virus as a trending topic on Twitter social media. Twitter was chosen by the public because it can disseminate information in real time and can see market reactions quickly. This research uses "tweet" data or public tweet related to "Corona Virus" to see how the sentiment polarity arises. Text mining techniques and three machine learning classification algorithms are used, including Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) to build a tweet classification model of sentiments whether they have positive, negative, or neutral polarity. The highest test results are generated by the Support Vector Machine (SVM) algorithm with an accuracy value of 76.21%, a precision value of 78.04%, and a recall value of 71.42%.Keywords: Machine Learning, Corona Virus, Twitter, Sentiment Analysis.

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