Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

Dewasa ini, media sosial berkembang pesat di internet, salah satu yang banyak digemari adalah Twitter. Berbagai topik ramai diperbincangkan di Twitter mulai dari ekonomi, politik, sosial, budaya, hukum dan lain-lain. Salah satu topik yang ramai diperbincangkan di Twitter adalah terkait isu pemindahan ibu kota Indonesia. Namun dibalik hal tersebut terdapat kontroversi dari pihak yang merasa pro dan kontra, masing-masing memiiki sudut pandang yang berbeda. Hal ini menyebabkan munculnya fenomena perdebatan khususnya di Twitter yang sebenarnya menunjukkan perhatian kolektif mengenai wacana publik tersebut. Analisis sentimen adalah proses mengekstraksi, memahami dan mengolah data berupa teks yang tidak terstruktur secara otomatis guna mendapatkan informasi sentimen yang terdapat pada sebuah kalimat pendapat atau opini. Dalam penerapan analisis sentimen menggunakan metode machine learning terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode Support Vector Machine (SVM) untuk diterapkan pada tweets topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial twitter. Teknis klasifikasi dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap tweets sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 tweets (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, precision=95.82%, recall=94.04% dan AUC = 0,979. AbstractToday, social media is growing fast on the internet.One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several methods that are often used. In this study, the Support Vector Machine (SVM) method is proposed to be applied to tweets on the topic of relocating Indonesia's capital city for sentiment classification on social media twitter. The classification technique is carried out into 2 classes, namely positive and negative. Based on testing on the sentiment of relocating Indonesia's capital city from social media twitter from 1,116 tweets (404 positive and 832 negative) using SVM obtained accuracy = 96.68%, precision = 95.82%, recall = 94.04% and AUC = 0.979.

Download Full-text

World Covid-19 Vaccine Names Classification Using Neural Network Method

Journal of Business and Technology ◽

10.24167/jbt.v1i1.3219 ◽

2021 ◽

Vol 1 (1) ◽

pp. 31

Author(s):

Kristiawan Nugroho

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Support Vector ◽

Neural Network Method ◽

The Public ◽

Machine Learning Methods ◽

The Neural Network ◽

The World ◽

Network Method

The Covid-19 pandemic has occurred for a year on earth. Various attempts have been made to overcome this pandemic, especially in making various types of vaccines developed around the world. The level of vaccine effectiveness in dealing with Covid-19 is one of the questions that is often asked by the public. This research is an attempt to classify the names of vaccines that have been used in various nations by using one of the robust machine learning methods, namely the Neural Network. The results showed that the Neural Network method provides the best accuracy, which is 99.9% higher than the Random Forest and Support Vector Machine(SVM) methods.

Download Full-text

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

Sensors ◽

10.3390/s22010155 ◽

2021 ◽

Vol 22 (1) ◽

pp. 155

Author(s):

Kristina Machova ◽

Marian Mach ◽

Matej Vasilko

Keyword(s):

Machine Learning ◽

Social Networks ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Online Discussions ◽

Support Vector ◽

Machine Accuracy ◽

Detection Model ◽

Machine Learning Methods ◽

The Difference

The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll’s comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.

Download Full-text

COVID-19 and Black Fungus: Analysis of the Public Perception through Machine Learning

10.1101/2021.07.08.21260188 ◽

2021 ◽

Author(s):

Muhammad Nazrul Islam ◽

Nafiz Imtiaz Khan ◽

Tahasin Mahmud

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Public Perception ◽

Public Perceptions ◽

Support Vector ◽

Machine Model ◽

The Public ◽

Class Labels ◽

Media Data

While COVID-19 is ravaging the lives of millions of people across the globe, a second pandemic 'black fungus' has surfaced robbing people of their lives especially people who are recovering from coronavirus. Again, the public perceptions regarding such pandemics can be investigated through sentiment analysis of social media data. Thus the objective of this study is to analyze public perceptions through sentiment analysis regarding black fungus during the time of the COVID-19 pandemic. To attain the objective, first, a Support Vector Machine model, with an average AUC of 82.75\%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this Support Vector Machine is used to supervise the class labels of the public tweets (n = 6477) related to COVID-19 and black fungus. As outcome, this study found that public perceptions belong to sad (n = 2370, 36.59 \%), followed by joy ( n = 2095, 32.34\%), fear ( n = 1914, 29.55 \%) and anger ( n = 98, 1.51\%) towards black fungus during COVID-19 pandemic. This study also investigated public perceptions of some critical concerns (e.g., education, lockdown, hospital, oxygen, quarantine, and vaccine) and it was found that public perceptions of these issues varied. For example, for the most part, people exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen.

Download Full-text

Sentiment Analysis on Corona Virus Pandemic Using Machine Learning Algorithm

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING ◽

10.31289/jite.v4i1.3798 ◽

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.

Download Full-text

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

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1840 ◽

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.

Download Full-text

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i3.1942 ◽

2020 ◽

Vol 4 (3) ◽

pp. 504-512

Author(s):

Faried Zamachsari ◽

Gabriel Vangeran Saragih ◽

Susafa'ati ◽

Windu Gata

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Feature Selection ◽

Public Opinion ◽

Naive Bayes ◽

Naïve Bayes ◽

Capital City ◽

Support Vector ◽

National Capital ◽

Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

Download Full-text

Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

Sustainability ◽

10.3390/su13063497 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3497

Author(s):

Hassan Adamu ◽

Syaheerah Lebai Lutfi ◽

Nurul Hashimah Ahamed Hassain Malim ◽

Rohail Hassan ◽

Assunta Di Vaio ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Primary Objective ◽

Support Vector ◽

Standard English ◽

Emotion Classification ◽

K Nearest Neighbor ◽

The Public ◽

The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

Download Full-text

A sentiment analysis system for social media using machine learning techniques: Social enablement

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Tool ◽

Data Set ◽

Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

Download Full-text

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

Jurnal Ilmiah FIFO ◽

10.22441/fifo.2019.v11i2.004 ◽

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.

Download Full-text

Twitter sentiment analysis of the relocation of Indonesia's capital city

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i4.2352 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1620-1630

Author(s):

Edi Sutoyo ◽

Ahmad Almaarif

Keyword(s):

Support Vector Machine ◽

Sentiment Analysis ◽

Nearest Neighbor ◽

Point Of View ◽

Capital City ◽

Support Vector ◽

K Nearest Neighbor ◽

Evaluation Algorithm ◽

The Government ◽

The Many

Indonesia has a capital city which is one of the many big cities in the world called Jakarta. Jakarta's role in the dynamics that occur in Indonesia is very central because it functions as a political and government center, and is a business and economic center that drives the economy. Recently the discourse of the government to relocate the capital city has invited various reactions from the community. Therefore, in this study, sentiment analysis of the relocation of the capital city was carried out. The analysis was performed by doing a classification to describe the public sentiment sourced from twitter data, the data is classified into 2 classes, namely positive and negative sentiments. The algorithms used in this study include Naïve Bayes classifier, logistic regression, support vector machine, and K-nearest neighbor. The results of the performance evaluation algorithm showed that support vector machine outperformed as compared to 3 algorithms with the results of Accuracy, Precision, Recall, and F-measure are 97.72%, 96.01%, 99.18%, and 97.57%, respectively. Sentiment analysis of the discourse of relocation of the capital city is expected to provide an overview to the government of public opinion from the point of view of data coming from social media.

Download Full-text