• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.334-342
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• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• International Journal of Contents
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• 제17권1호
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• pp.1-10
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• 2021

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins' prices were high. Emotional language strategies on social media did not affect cryptocurrencies' reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.71-84
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• 2024

This paper presents a critical analysis of the current application of big data in higher education and how Learning Analytics (LA), and Educational Data Mining (EDM) are helping to shape learning in higher education institutions that have applied the concepts successfully. An extensive literature review of Learning Analytics, Educational Data Mining, Learning Management Systems, Informal Learning and Online Social Networks are presented to understand their usage and trends in higher education pedagogy taking advantage of 21st century educational technologies and platforms. The roles of and benefits of these technologies in teaching and learning are critically examined. Imperatively, this study provides vital information for education stakeholders on the significance of establishing a teaching and learning agenda that takes advantage of today's educational relevant technologies to promote teaching and learning while also acknowledging the difficulties of 21st-century learning. Aside from the roles and benefits of these technologies, the review highlights major challenges and research needs apparent in the use and application of these technologies. It appears that there is lack of research understanding in the challenges and utilization of data effectively for learning analytics, despite the massive educational data generated by high institutions. Also due to the growing importance of LA, there appears to be a serious lack of academic research that explore the application and impact of LA in high institution, especially in the context of informal online social network learning. In addition, high institution managers seem not to understand the emerging trends of LA which could be useful in the running of higher education. Though LA is viewed as a complex and expensive technology that will culturally change the future of high institution, the question that comes to mind is whether the use of LA in relation to informal learning in online social network is really what is expected? A study to analyze and evaluate the elements that influence high usage of OSN is also needed in the African context. It is high time African Universities paid attention to the application and use of these technologies to create a simplified learning approach occasioned by the use of these technologies.

• Journal of Information Technology Applications and Management
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• 제22권1호
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• pp.37-60
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• 2015

As the dependence on information systems in enterprises has grown dramatically, the importance of implementing information systems in audit has been increased as well. However, there is a lact of about utilization of information system for audit process. Thus, this study is to investigate the factors that effect auditor's adopting Data Analytics to audit work. Through literature research and focus group interview, we added two factors that affect the behavioral intention to UTAUT model. We have selected performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety, task fit, behavioral intention as variables and verified hypotheses based on survey questionnaires from auditors. As a result, it was found that performance expectations, social influence, task fit influenced the behavior intention. In Addition, we analyzed adding two variables, IT-related work experience and type of auditor as moderate variable. This study has an implication for companies to motivate implementation as well as activation of Data Analytics technique.

• 디지털콘텐츠학회 논문지
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• 제13권2호
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• pp.205-211
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• 2012

본 논문은 기업의 마케팅 캠페인 실행 성과를 향상시키기 위한 디지털 마케팅 모델에 대한 연구 결과를 제시하고 있다. 최근 ERP, CRM, SCM 등 비즈니스 가치 사슬 프로세스를 개선하기 위한 프로젝트를 마친 기업들은 마케팅 프로세스를 전사적으로 개선하기 위한 작업을 진행하고 있다. 기존 마케팅 기법의 한계를 극복하기 위해서 디지털 마케팅 기법을 활용하기 위한 시도가 많다. 특히 마케팅 프로세스의 실행 단계인 캠페인 수행성과를 향상시키기 위해 디지털 마케팅 기법들을 적용하고 있다. 이에 본 논문에서는 디지털 마케팅 연구 모델과 연구 가설을 수립하고, 마케팅 전문가 설문 조사를 통한 통계적 분석 방법을 통해 검증하고자 하였다. 연구를 통해서 디지털 마케팅 모델 중에서 웹 분석, 소셜 분석, 개인 맞춤형 고객 관계 분석, 캠페인 실행 자동화, 실시간 캠페인 관리 등의 기법이 기업의 마케팅 캠페인 실행 성과에 영향을 미치는 것을 실증적으로 검증하였다.

• 한국안전학회지
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• 제32권3호
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• pp.83-89
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• 2017

A new technology has provided the nation, industry, society, and people with innovative and useful functions. National economy and society has been improved through this technology innovation. Despite the benefit of technology innovation, however, since technology society was sufficiently mature, the unintended side effect and negative impact of new technology on society and human beings has been highlighted. Thus, it is important to investigate a risk of new technology for the future society. Recently, the risks of the new technology are being suggested through a large amount of social data such as news articles and report contents. These data can be used as effective sources for quantitatively and systematically forecasting social risks of new technology. In this respect, this paper aims to propose a data-driven process for forecasting and assessing social risks of future new technology using the text mining, 4M(Man, Machine, Media, and Management) framework, and analytic hierarchy process (AHP). First, social risk factors are forecasted based on social risk keywords extracted by the text mining of documents containing social risk information of new technology. Second, the social risk keywords are classified into the 4M causes to identify the degree of risk causes. Finally, the AHP is applied to assess impact of social risk factors and 4M causes based on social risk keywords. The proposed approach is helpful for technology engineers, safety managers, and policy makers to consider social risks of new technology and their impact.

• 정보과학회 논문지
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• 제42권6호
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• pp.781-790
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• 2015

인터넷과 모바일 환경의 발전에 따라 최근에는 마이크로블로그가 성행하고 있다. 마이크로블로그에는 부가적인 데이터가 담겨있다. 그 중 위치 정보에 대한 데이터를 포함하는 마이크로블로그 데이터를 공간 소셜 웹 객체라고 지칭한다. 이러한 마이크로블로그 데이터에 대한 일반 집계는 사용자별 데이터 집계 등이 있으나, 단일 정보에 대한 집계만 가능하다. 본 연구는 공간 소셜 웹 객체의 특성을 갖는 마이크로블로그 데이터의 공간 소셜 분석을 위해, 일반 집계와 공간 데이터를 결합하고 지오해시와 맵리듀스를 이용한 공간 집계에 대한 알고리즘을 제시한다. 이를 통해 의미있는 공간 소셜에 대한 분석의 기반을 마련하였다.

• 산경연구논집
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• 제13권11호
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• pp.1-10
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• 2022

Purpose: This study measures the influence of facilitating conditions on employees' attitudes towards the adoption of big data analytics by selected medical aid organizations in Durban. In the health care sector, there are various sources of big data such as patients' medical records, medical examination results, and pharmacy prescriptions. Several organizations take the benefits of big data to improve their performance and productivity. Research design, data, and methodology: A survey research strategy was conducted on some selected medical aid organizations. A non-probability sampling and the purposive sampling technique were adopted in this study. The collected data was analysed using version 23 of Statistical Package for Social Science (SPSS) Results: the results show that the "facilitating conditions" have a positive influence on employees' attitudes in the adoption of big data analytics Conclusions: The findings of this study provide empirical and scientific contributions of the facilitating conditions issues regarding employee attitudes toward big data analytics adoption. The findings of this study will add to the body of knowledge in this field and raise awareness, which will spur further research, particularly in developing countries.

• Educational Technology International
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• 제17권1호
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• pp.117-150
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• 2016

The purpose of this study was to develop design guidelines on the learning analytics which can help to promote students' self-regulated learning (SRL) strategies in MOOCs learning environments. First of all, to develop the first draft of design guidelines, relevant literature review and case analysis on current MOOCs platforms such as edX, K-MOOC, Coursera, Khan Academy and FutureLearn were conducted. Then, to validate the design guidelines, expert reviews (validation questionnaires and in-depth interviews) and learner evaluation (in-depth interviews) were conducted. Through the recursive validation, the design guidelines were finalized. Overall, the final version of design guidelines on learning analytics to facilitate SRL strategies was suggested. The final design guidelines consist of 15 items in 10 categories related to the information analyzed based on individual student's learning behaviors and activities on MOOCs environments. Moreover, the results of interview also revealed that the social comparisons, learning progress reports, and personalization might contribute to the improvements of their SRL competences. This study has an implication that MOOCs could offer a higher success or completion rate to students with low SRL skills by taking advantage of the information on learning analytics

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.391-406
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• 2015

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.