The construction of a sentiment dictionary for public opinion mining on social media: a study on nuclear power opinion on Twitter

2015 ◽  
Author(s):  
D. S. Kim ◽  
J. W. Kim
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Nuclear Power ◽  
Opinion Mining ◽  
Sentiment Dictionary

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 Detection of Sentiment Analysis in Social Media using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 636-643
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Activation Function ◽  
Sentiment Classification ◽  
Network Layers ◽  
Sentiment Dictionary ◽  
Lstm Network ◽  
The Military

With the rapid climb of web page from social media, such studies as online opinion mining or sentiment analysis of text have started receiving attention from government, industry, and academic sectors. In recent years, sentiment analysis has not only emerged under knowledge fusion within the big data era, but has also become a well-liked research topic within the area of AI and machine learning. This study used the Military life PTT board of Taiwan’s largest online forum because the source of its experimental data. the aim of this study was to construct a sentiment analysis framework and processes for social media so as to propose a self-developed military sentiment dictionary for improving sentiment classification and analyze the performance of various deep learning models with various parameter calibration combinations. The experimental results show that the accuracy and F1-measure of the model that mixes existing sentiment dictionaries and therefore the self-developed military sentiment dictionary are better than the results from using existing sentiment dictionaries only. Furthermore, the prediction model trained using the activation function, Tanh, and when the amount of Bi-LSTM network layers is 2, the accuracy and F1-measure have a good better performance for sentiment classification.

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 Exploring new ways to utilise the market intelligence (MI) function in corporate decisions: Case opinion mining of nuclear power

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kalle Petteri Nuortimo ◽  
Janne Härkönen
Keyword(s):  
Social Media ◽  
Strategic Planning ◽  
Technology Management ◽  
Nuclear Power ◽  
Opinion Mining ◽  
Brand Image ◽  
Business Environment ◽  
Corporate Decisions ◽  
Media Sentiment ◽  
Competitor Intelligence

The challenge in today’s corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue—falling as a topic mainly under the area of technology management—technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company’s image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company’s images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company’s communication and branding activities. This paper has implications showing that a technology’s media sentiment can influence a company’s brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media.

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%.

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