scholarly journals Public Sentiments Analysis about Indonesian Social Insurance Administration Organization on Twitter

2020 ◽  
Vol 9 (2) ◽  
pp. 87-93
Author(s):  
Siti Rahmawati ◽  
Muhammad Habibi
Keyword(s):  
Support Vector Machine ◽  
Social Insurance ◽  
Negative Polarity ◽  
Support Vector ◽  
Bayes Classifier ◽  
The Public ◽  
Public Sentiment ◽  
Data Collection Period ◽  
Collection Period ◽  
Tweet Classification

Insurance Administration Organization, which can be used by all people. However, this organization has received various criticisms from the public through social media, namely Twitter. This study aims to analyze public sentiment about the Indonesian Social Insurance Administration Organization on Twitter. The method used in this research is the Naive Bayes Classifier (NBC) method and uses the Support Vector Machine (SVM) method as a comparison. The amount of data used was 12,990 tweets with a data collection period from September 14, 2019 - February 18, 2020. The study compared the two classifier models built, namely the classifier model with two sentiment classes and four sentiment classes. The accuracy results show that the SVM method has a better accuracy value than the NBC method. SVM has an accuracy value of 63.60% and 82.77% for the two sentiment classes in the four sentiment classifier model. The tweet classification results show that the public's conversation about the Indonesian Social Insurance Administration Organization on Twitter has a negative polarity value tendency.

scholarly journals SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER

2021 ◽  
Vol 2 (2) ◽  
pp. 101-107
Author(s):  
Akhmad Muzaki ◽  
Arita Witanti
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Local Elections ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Public Sentiment

The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%

scholarly journals Surabaya Government Performance Evaluation Using Tweet Analysis

MATEMATIKA ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 31-42
Author(s):  
Rakhmah Wahyu Mayasari ◽  
Kartika Fithriasari ◽  
Nur Iriawan ◽  
Wiwiek Setya Winahju
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Traffic Congestion ◽  
Classification Accuracy ◽  
Support Vector ◽  
Government Performance ◽  
Police Performance ◽  
Bayes Classifier ◽  
The Public ◽  
Pros And Cons

The purpose of this research is to determine the various positive attributes appreciated by the public, and the negative things that need to be improved by the Surabaya government. The sentiment analysis methods, including the Naïve Bayes Classifier, Support Vector Machine, and Logistic Regression, are employed to classify the pros and cons of the Surabaya government. The comparison of the three methods demonstrated that SVM gives the best classification accuracy compared to others. Police performance is the highlighted word in the positive category, while traffic congestion is in the negative.

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

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.

scholarly journals Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine

2014 ◽  
Vol 8 (1) ◽  
pp. 91 ◽  
Author(s):  
Noviah Dwi Putranti ◽  
Edi Winarko
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Social Networking Site ◽  
Training Data ◽  
Classification Model ◽  
Support Vector ◽  
Public Sentiment ◽  
Pos Tagger ◽  
Negative Sentiment ◽  
Bahasa Indonesia

AbstrakAnalisis sentimen dalam penelitian ini merupakan proses klasifikasi dokumen tekstual ke dalam dua kelas, yaitu kelas sentimen positif dan negatif.  Data opini diperoleh dari jejaring sosial Twitter berdasarkan query dalam Bahasa Indonesia. Penelitian ini bertujuan untuk menentukan sentimen publik terhadap objek tertentu yang disampaikan di Twitter dalam bahasa Indonesia, sehingga membantu usaha untuk melakukan riset pasar atas opini publik. Data yang sudah terkumpul dilakukan proses preprocessing dan POS tagger untuk menghasilkan model klasifikasi melalui proses pelatihan. Teknik pengumpulan kata yang memiliki sentimen dilakukan dengan pendekatan berdasarkan kamus, yang dihasilkan dalam penelitian ini berjumlah 18.069 kata. Algoritma Maximum Entropy digunakan untuk POS tagger dan algoritma yang digunakan untuk membangun model klasifikasi atas data pelatihan dalam penelitian ini adalah Support Vector Machine. Fitur yang digunakan adalah unigram dengan fitur pembobotan TFIDF. Implementasi klasifikasi diperoleh akurasi 86,81 %  pada pengujian 7 fold cross validation untuk tipe kernel Sigmoid. Pelabelan kelas secara manual dengan POS tagger menghasilkan akurasi 81,67%.  Kata kunci—analisis sentimen, klasifikasi, maximum entropy POS tagger, support vector machine, twitter.  AbstractSentiment analysis in this research classified textual documents into two classes, positive and negative sentiment. Opinion data obtained a query from social networking site Twitter of Indonesian tweet. This research uses  Indonesian tweets. This study aims to determine public sentiment toward a particular object presented in Twitter businesses conduct market. Collected data then prepocessed to help POS tagged to generate classification models through the training process. Sentiment word collection has done the dictionary based approach, which is generated in this study consists 18.069 words. Maximum Entropy algorithm is used for POS tagger and the algorithms used to build the classification model on the training data is Support Vector Machine. The unigram features used are the features of TFIDF weighting.Classification implementation 86,81 % accuration at examination of 7 validation cross fold for the type of kernel of Sigmoid. Class labeling manually with POS tagger yield accuration 81,67 %. Keywords—sentiment analysis, classification, maximum entropy POS tagger, support vector machine, twitter.

scholarly journals IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI SENTIMEN REVIEW LAYANAN TELEMEDICINE HALODOC

2021 ◽  
Vol 2 (2) ◽  
pp. 96-104
Author(s):  
REYNALDA NABILA CIKANIA
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Telemedicine Service ◽  
Application Service ◽  
Auc Value

Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service

scholarly journals NAIVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE SEBAGAI ALTERNATIF SOLUSI UNTUK TEXT MINING

2019 ◽  
Vol 12 (2) ◽  
pp. 32-38
Author(s):  
Iin Ernawati
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Relationship

This study was conducted to text-based data mining or often called text mining, classification methods commonly used method Naïve bayes classifier (NBC) and support vector machine (SVM). This classification is emphasized for Indonesian language documents, while the relationship between documents is measured by the probability that can be proven with other classification algorithms. This evident from the conclusion that the probability result Naïve Bayes Classifier (NBC) word “party” at least in the economic document and political. Then the result of the algorithm support vector machine (svm) with the word “price” and “kpk” contains in both economic and politic document.  

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