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

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.

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

2021 ◽  
Vol 8 (1) ◽  
pp. 147
Author(s):  
Primandani Arsi ◽  
Retno Waluyo
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Public Discourse ◽  
Capital City ◽  
Support Vector ◽  
The Public ◽  
Machine Learning Methods ◽  
Classification Technique

<p class="Abstrak">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 <em>machine learning</em> terdapat beberapa metode yang sering digunakan. Dalam penelitian ini diusulkan metode <em>Support Vector Machine</em> (SVM) untuk diterapkan pada <em>tweets</em> topik pemindahan ibu kota Indonesia untuk tujuan klasifikasi kelas sentimen pada media sosial <em>twitter</em>. Teknis klasifikasi  dilakukan dengan cara mengklasifikasikan menjadi 2 kelas yakni positif dan negatif. Berdasarkan hasil pengujian yang dilakukan terhadap <em>tweets</em> sentimen pemindahan ibu kota dari media sosial twitter sebanyak 1.236 <em>tweets</em> (404 positif dan 832 negatif) menggunakan SVM diperoleh akurasi =96,68%, <em>precision=</em>95.82%, <em>recall</em>=94.04% dan AUC = 0,979.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Today, social media is growing fast on the internet<span lang="EN-GB">.</span><span lang="EN-GB">On</span>e of the most popular<span lang="EN-GB"> social media</span> is Twitter. Many topics are discussed on Twitter such as economic, politic, socia<span lang="EN-GB">l</span>, cultur<span lang="EN-GB">e</span>, <span lang="EN-GB">and l</span>aw<span lang="EN-GB">.</span> One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However<span lang="EN-GB">, </span>there is controversy from supporters and opponents<span lang="EN-GB">. They</span> have different views. <span lang="EN-GB">This issue leads to</span> a phenomenon of debate on Twitter <span lang="EN-GB">that </span>actually show<span lang="EN-GB">s a </span>collective concern about the public discourse. Sentiment analysis is a process of extracting, understand<span lang="EN-GB">ing </span>and process<span lang="EN-GB">ing</span> unstructured data to get sentiment information which is<span lang="EN-GB"> found</span> in an opinion sentence. Application of sentiment analysis using machine learning methods<span lang="EN-GB"> shows that</span> 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.</em></em></p>

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 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 PUBLIC PERCEPTION OF IMMIGRATION CONTENT ON SOCIAL MEDIA INSTAGRAM AT THE TPI EAST JAKARTA CLASS I IMMIGRATION OFFICE

2019 ◽  
Vol 1 (2) ◽  
pp. 81-92
Author(s):  
Muhammad Aditya Majdi
Keyword(s):  
Qualitative Research ◽  
Social Media ◽  
Research Method ◽  
Public Perception ◽  
Public Perceptions ◽  
Class I ◽  
Learning Material ◽  
Quality Of Information ◽  
The Public

Public perception of immigration content at the TPI East Jakarta Class I Immigration Office is very important in determining the quality of information and understanding of immigration provided to the public by focusing on social media Instagram. With some literacy regarding public perceptions it can produce a public view of immigration content that has been disseminated through social media Instagram TPI Class I Immigration Office, East Jakarta. This can be used as study and learning material in seeing some of the shortcomings that must be addressed by the TPI East Jakarta Class I Immigration Office regarding public perceptions of immigration content. With the descriptive qualitative research method, it explains that there are still gaps or shortcomings of immigration content disseminated through social media Instagram TPI Class I Immigration Office, East Jakarta. So it is very necessary to make several further research studies related to public perceptions of immigration content so as to harmonize understanding between the information provider and the recipient of the information.  

Sentiment Analysis with Social Media Analytics, Methods, Process, and Applications

2017 ◽  
pp. 192-208
Author(s):  
Karteek Ramalinga Ponnuru ◽  
Rashik Gupta ◽  
Shrawan Kumar Trivedi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Social Media Analytics ◽  
Nearest Neighbour ◽  
Huge Amount ◽  
Word Clouds

Firms are turning their eye towards social media analytics to get to know what people are really talking about their firm or their product. With the huge amount of buzz being created online about anything and everything social media has become ‘the' platform of the day to understand what public on a whole are talking about a particular product and the process of converting all the talking into valuable information is called Sentiment Analysis. Sentiment Analysis is a process of identifying and categorizing a piece of text into positive or negative so as to understand the sentiment of the users. This chapter would take the reader through basic sentiment classifiers like building word clouds, commonality clouds, dendrograms and comparison clouds to advanced algorithms like K Nearest Neighbour, Naïve Biased Algorithm and Support Vector Machine.

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 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 Modified framework for sarcasm detection and classification in sentiment analysis

2019 ◽  
Vol 13 (3) ◽  
pp. 1175 ◽  
Author(s):  
Mohd Suhairi Md Suhaimin ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Rayner Alfred ◽  
Frans Coenen
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Social Media Data ◽  
Classification Framework ◽  
Media Data ◽  
F Measure

<span>Sentiment analysis is directed at identifying people's opinions, beliefs, views and emotions in the context of the entities and attributes that appear in text. The presence of sarcasm, however, can significantly hamper sentiment analysis. In this paper a sentiment classification framework is presented that incorporates sarcasm detection. The framework was evaluated using a non-linear Support Vector Machine and Malay social media data. The results obtained demonstrated that the proposed sarcasm detection process could successfully detect the presence of sarcasm in that better sentiment classification performance was recorded. A best average F-measure score of 0.905 was recorded using the framework; a significantly better result than when sentiment classification was performed without sarcasm detection.</span>

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

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