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>

Opinion mining in online social media: a survey

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Chaima Messaoudi ◽  
Zahia Guessoum ◽  
Lotfi Ben Romdhane
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Online Social Media

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 Sentiment Analysis Using Random Forest Algorithm-Online Social Media Based

2019 ◽  
Vol 2 (2) ◽  
pp. 29
Author(s):  
Nfn Bahrawi
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Community Services ◽  
Random Forest Algorithm ◽  
The Public ◽  
Online Social Media ◽  
Accuracy Of Measurements

Every day billions of data in the form of text flood the internet be it sourced from forums, blogs, social media, or review sites. With the help of sentiment analysis, previously unstructured data can be transformed into more structured data and make this data important information. The data can describe opinions / sentiments from the public, about products, brands, community services, services, politics, or other topics. Sentiment analysis is one of the fields of Natural Language Processing (NLP) that builds systems for recognizing and extracting opinions in text form. At the most basic level, the goal is to get emotions or 'feelings' from a collection of texts or sentences. The field of sentiment analysis, or also called 'opinion mining', always involves some form of data mining process to get the text that will later be carried out the learning process in the mechine learning that will be built. this study conducts a sentimental analysis with data sources from Twitter using the Random Forest algorithm approach, we will measure the evaluation results of the algorithm we use in this study. The accuracy of measurements in this study, around 75%. the model is good enough. but we suggest trying other algorithms in further research. Keywords: sentiment analysis; random forest algorithm; clasification; machine learnings. 

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 Truth Discovery in Big Data Social Media Sensing Applications

2020 ◽  
Vol 9 (8) ◽  
pp. 999-1004
Keyword(s):  
Social Media ◽  
Big Data ◽  
False Information ◽  
Distributed Structure ◽  
Sensing Applications ◽  
Online Social Media ◽  
Truth Discovery ◽  
Novel Approach ◽  
Work Queue ◽  
The Impact

The detection of truthful information amid data provided by online social media platforms (e.g., Twitter, Facebook, Instagram) is a critical task in the trend of big data. Truth Discovery is nothing but the extraction of true information or facts from unwanted and raw data, which has become a difficult task nowadays in today's day and age due to the rampant spread of rumors and false information. Before posting anything on the social media platform, people do not consider fact-checking and the source authenticity and frantically spread them by re-posting them which has made the detection of truthful claims more difficult than ever. So, this problem needs to be addressed soon since the impact of false information and misunderstanding can be very powerful and misleading. This mission, truth discovery, is targeted at establishing the authenticity of the sources and therefore the truthfulness of the statements that they create without knowing whether it is true or not. We propose a Big Data Truth Discovery Scheme (BDTD) to overcome the major problems. We have three major problems, the main one being "False information spread" where a large number of sources lead to false or fake statements, making it difficult to distinguish true statements, now this problem is solved by our scheme by studying the various behaviors of sources. On Twitter for example rumormongering is common. The second problem is "lack of claims" where most outlets contribute only a tiny small number of claims, giving very few pieces of evidence and making it not sufficient to analyze the trustworthiness of such sources, this problem is addressed by our scheme where it uses an algorithm that evaluates the claim’s truthfulness and historic contributions of the source regarding the claim. Thirdly the scalability challenge, due to the clustered design of their existing truth discovery algorithms, many existing approaches don't apply to Big-scale social media sensing cases so this challenge is managed by our scheme by making use of frameworks HTCondor and Work Queue. This scheme computes both the reliability of the sources and, ultimately, the legitimacy of statements using a novel approach. A distributed structure is also developed for the implementation of the proposed scheme by making use of the Work Queue (platform) in the HTCondor method (maybe distributed). Findings of the test on a real-world dataset indicate that the BDTD system greatly outperforms the existing methods of Discovery of Truth both in terms of performance and efficiency.

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