scholarly journals Public Opinion Mining on Social Media

IJARCCE ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 934-937
Author(s):  
Reema D ◽  
J Nagesh Babu
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Opinion Mining

scholarly journals A Study on the Decision-Making of Effective S/W Education based on Opinion Mining Analysis

2018 ◽  
Vol 7 (4.16) ◽  
pp. 5-9
Author(s):  
Ji-Hoon Seo ◽  
Ji-Hoon Seo ◽  
Nam-Hun Park ◽  
Kil-Hong Joo
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Opinion Mining ◽  
Ministry Of Education ◽  
Education Policies ◽  
The Public ◽  
Evaluation Functions ◽  
Effective Policy ◽  
Policy Agendas ◽  
Set Up

The Currently, along with the advent of the web 2.0 era, due to the continuous expansion of social media service infrastructures, the shares of conventional public opinion evaluation functions have been gradually shifting from the existing mass media to social media. This phe-nomenon is attributable to the two-way communication and convenience unique to social media and social media are now in charge of an axis of public opinion evaluation standards. In particular, since diverse interests conflict in education policies and countless conflicts of opinions occur in the process of setting up policy agendas, in establishing education policies, accurately analyzing reputations among the public, who are the targets of education policies, in order to set up effective policy agendas, is the most important issue. Therefore, in this study, the resultant values of huge unstructured data on the positive and negative reputations of past policy agendas related to the mandatory software education that has been organized as a regular curriculum of middle/high schools from 2018 in Korea, which have been addressed by the Ministry of Education, the Ministry of Science, ICT and Future Planning, and the Korea Foundation for the Advancement of Science and Creativity, felt and judged by the general public on social media such as blogs and Twitter and on online media including portal news were visualized through opinion mining analysis techniques to derive more effective software education related policy agendas. In addition, based on the foregoing, a Korean style software education system that fits circumstances was constructed and the system is expected to become an important measure that provides guidelines for setting mid/long-term road maps for the fostering of creative and convergent talented persons equipped with international competitiveness and software education in Korea.  

scholarly journals Teknologi Opinion Mining untuk Mendukung Strategic Planning

2020 ◽  
Vol 7 (2) ◽  
pp. 293
Author(s):  
Dwi Rolliawati ◽  
Khalid Khalid ◽  
Indri Sudanawati Rozas
Keyword(s):  
Social Media ◽  
Big Data ◽  
Public Opinion ◽  
Strategic Planning ◽  
Opinion Mining ◽  
Islamic State ◽  
State University ◽  
Online Social Media ◽  
Stop Word ◽  
World Class

<p class="Abstrak">Banjir data di era Big Data sudah tidak bisa terelakkan lagi. Termasuk di dalamnya data yang sangat melimpah di media sosial daring. Peluang inilah yang ditangkap sebagai alasan utama pada penelitian ini. <em>Opinion mining</em> sebagai salah satu teknologi dalam mengolah data teks untuk memperoleh arah informasi dari komentar/opini masyarakat. Mengambil obyek penelitian UIN Sunan Ampel Surabaya, penelitian ini bertujuan untuk menganalisis opini masyarakat tentang kampus Islam terbesar di Surabaya. Sehingga bisa menjadi pendukung keputusan bagi pihak manajemen untuk merumuskan perencanaan strategis terwujudnya visi <em>World Class University</em>. Penelitian ini menggunakan 4009 data sampel berbahasa Indonesia yang diambil dari opini masyarakat di media sosial Twitter dalam kurun waktu dua tahun terakhir (2017 – 2018). Dari 4009 data dihasilkan 31837 jenis kata setelah melalui proses <em>stop-word removal</em>. Berdasarkan analisis <em>sentiment</em> menggunakan pendekatan Vader dan Liu yang divisualisasikan melalui grafik K-Means, dihasilkan bahwa opini publik terhadap UIN Sunan Ampel mengarah pada sentimen ’netral’ sebesar 97,54%, sedangkan sentiment positif =2,16%, dan sentiment negatif = 0,34%. Hasil tersebut membuktikan bahwa <em>Information Capital</em> tentang UIN Sunan Ampel perlu diperkuat menuju nilai “positif”. Sehingga diperlukan upaya maksimal untuk membangun <em>innovation and commercially supremacy, perception (public relation)</em> dan <em>scalability strategies</em> supaya <em>internal operation</em> bisa handal untuk ketercapaian visi misi UIN Sunan Ampel Surabaya.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Data deluge in Big Data era is inevitable, this including a very abundant data in online social media. This phenomenon  was chosen as the main background reason in this research. Opinion mining is as one of the technologies in processing text data to obtain information direction from public comments/opinions. Taking the object of research at Sunan Ampel Islamic State University Surabaya, this study aims to analyze public community opinion toward the biggest Islamic campus in Surabaya. Hopefully,  it would be beneficial as decisional support for management in formulating strategic planning to manifest the World Class University vision. This study uses 4009 Indonesian language sample data taken from public opinion on Twitter social media in the past two years (2017 - 2018). Out from 4009 data, 31837 types of words are obtained after going through a stop-word removal process. Based on sentiment analysis by Vader and Liu’s approach which was visualized by K-Means graphs, the finding was that 97,54% of public opinion toward Sunan Ampel Islamic State University Surabaya led to a 'neutral' sentiment, while positive = 2,16% and negative=0,34%. These results prove that Information Capital about Sunan Ampel UIN needs to be strengthened towards "positive" image. For this reason, maximum effort is needed to build innovation and commercialization of supremacy, perception (public relations) and scalability strategies so that internal operations can be reliable in achieving the vision of Sunan Ampel Islamic State University Surabaya.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Mining Public Opinion about Economic Issues

2018 ◽  
Vol 9 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Amir Karami ◽  
London S. Bennett ◽  
Xiaoyun He
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Text Mining ◽  
Opinion Mining ◽  
Social Media Data ◽  
Economic Issues ◽  
Election Prediction ◽  
Us Presidential Election ◽  
Mining Methods ◽  
Media Data

Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

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

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%.

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

Communicology ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 167-179
Author(s):  
E.S. Nadezhkina
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Public Diplomacy ◽  
User Generated Content ◽  
The Internet ◽  
Interactive Communication ◽  
Network Interaction ◽  
Social Media Platforms ◽  
The Right ◽  
Chinese Social Media

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

scholarly journals COVID-19 conspiracy theories

2021 ◽  
Vol 24 (2) ◽  
pp. 270-275 ◽  
Author(s):  
Karen M. Douglas
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Intergroup Relations ◽  
Group Processes ◽  
Negative Consequences ◽  
Conspiracy Theories ◽  
Open Questions ◽  
Potential Impact

Conspiracy theories started to appear on social media immediately after the first news about COVID-19. Is the virus a hoax? Is it a bioweapon designed in a Chinese laboratory? These conspiracy theories typically have an intergroup flavour, blaming one group for having some involvement in either manufacturing the virus or controlling public opinion about it. In this article, I will discuss why people are attracted to conspiracy theories in general, and why conspiracy theories seem to have flourished during the pandemic. I will discuss what the consequences of these conspiracy theories are for individuals, groups, and societies. I will then discuss some potential strategies for addressing the negative consequences of conspiracy theories. Finally, I will consider some open questions for research regarding COVID-19 conspiracy theories, in particular focusing on the potential impact of these conspiracy theories for group processes and intergroup relations.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

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