scholarly journals ANALISIS SENTIMEN PUBLIK PADA MEDIA SOSIAL TWITTER TERHADAP PELAKSANAAN PILKADA SERENTAK MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

CCIT Journal ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 197-206
Author(s):  
Atika Rahmawati ◽  
Aris Marjuni ◽  
Junta Zeniarja
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Public Response ◽  
The People ◽  
Word Detection ◽  
Twitter Users ◽  
Media Sentiment

Pilkada Serentak is a very important event for the future viability regions and countries. Through this election people can cast their vote and elect representatives of the people according to their choice. Public respond can be expressed through twitter social media. Using twitter social media sentiment analysis can then be made about the public response to the implementation of the election simultaneously. The classification process can be detected via text tweeted by twitter users. In this study, the classification of responses detected by text because it is easily obtained and applied. This study determined the classification of the response to the Indonesian language text and increase accuracy by using SVM.Tweet classification method used by the categorical approach is divided into two classes tweet basic level: positive and negative. Data collected from Indonesian twitter tweet as much as 3000. The labeling is not done manually but using clustering method that divides the 3000 data into two groups. Cluster 1 as a group of positive tweets and Cluster 2 as a negative group tweet.2700 for training data and 300 for the test data. The stage of pre-processing the data includetokenization, casenormalization, stop word detection, and stemming. The process of classification using Support Vector Machine (SVM). Accuracy of SVM showed the highest yield that is 91% compared to the k-means clustering with the results of 82%.

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

scholarly journals Hate Speech Detection for Indonesia Tweets Using Word Embedding And Gated Recurrent Unit

2019 ◽  
Vol 13 (1) ◽  
pp. 43
Author(s):  
Junanda Patihullah ◽  
Edi Winarko
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Hate Speech ◽  
Support Vector ◽  
Speech Detection ◽  
The People ◽  
Gated Recurrent Unit ◽  
Rule Out

Social media has changed the people mindset to express thoughts and moods. As the activity of social media users increases, it does not rule out the possibility of crimes of spreading hate speech can spread quickly and widely. So that it is not possible to detect hate speech manually. GRU is one of the deep learning methods that has the ability to learn information relations from the previous time to the present time. In this research feature extraction used is word2vec, because it has the ability to learn semantics between words. In this research the GRU performance will be compared with other supervision methods such as support vector machine, naive bayes, decision tree and logistic regression. The results obtained show that the best accuracy is 92.96% by the GRU model with word2vec feature extraction. The use of word2vec in the comparison supervision method is not good enough from tf and tf-idf.

scholarly journals Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes

2019 ◽  
Vol 13 (3) ◽  
pp. 263
Author(s):  
Nur Azizul Haqimi ◽  
Nur Rokhman ◽  
Sigit Priyanta
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Test Data ◽  
Training Data ◽  
Classification Method ◽  
Support Vector ◽  
Test Results ◽  
Imbalanced Dataset ◽  
Web Based ◽  
F Measure

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.

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 Classification of Spam Text using SVM

2021 ◽  
Vol 23 (08) ◽  
pp. 616-624
Author(s):  
Gaddam Akhil Reddy ◽  
◽  
Dr. B. Indira Reddy ◽  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Text Messages ◽  
Training Data ◽  
Support Vector ◽  
Spam Detection ◽  
Advantages And Disadvantages ◽  
Machine Learning Model ◽  
Model Support

The necessity for spam detection is particularly pertinent nowadays, as there is no quality control over social media, and users have the ability to distribute unverified material, therefore facilitating fraud and deceit. Spam detection can aid in the prevention of such fraud. This scenario has developed mostly as a result of the distribution of disparate, unconfirmed information via shopping websites, emails, and text messages (SMS). There are several ways of categorising and identifying spam. Each of them has certain advantages and disadvantages. The machine learning model “Support Vector Machine” is employed to detect spam in this case. SVM is a basic concept: the method proposes a line or hyperplane to classify the data. The model can categorise any type of text into a given category after being fed a set of labelled training data for each category.

scholarly journals Measuring information credibility in social media using combination of user profile and message content dimensions

2020 ◽  
Vol 10 (4) ◽  
pp. 3537
Author(s):  
Erwin B. Setiawan ◽  
Dwi H. Widyantoro ◽  
Kridanto Surendro
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Field Data ◽  
User Profile ◽  
Real Field ◽  
Support Vector ◽  
Message Content ◽  
Information Credibility

Information credibility in social media is becoming the most important part of information sharing in the society. The literatures have shown that there is no labeling information credibility based on user competencies and their posted topics. This study increases the information credibility by adding new 17 features for Twitter and 49 features for Facebook. In the first step, we perform a labeling process based on user competencies and their posted topic to classify the users into two groups, credible and not credible users, regarding their posted topics. These approaches are evaluated over ten thousand samples of real-field data obtained from Twitter and Facebook networks using classification of Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (Logit) and J48 algorithm (J48). With the proposed new features, the credibility of information provided in social media is increasing significantly indicated by better accuracy compared to the existing technique for all classifiers.

TrCSVM: a novel approach for the classification of melanoma skin cancer using transfer learning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Support Vector Machine ◽  
Skin Lesion ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Training Data ◽  
Support Vector ◽  
Second Phase ◽  
Target Domain ◽  
Content Type

PurposeThe study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.Design/methodology/approachIn this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.FindingsThe experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.Originality/valueExperiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

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