scholarly journals Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management

2016 ◽  
Author(s):  
Weng-Kun Liu ◽  
Chia-Chun Yen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Public Sector ◽  
Environmental Sustainability ◽  
Research Area ◽  
Big Data Analysis ◽  
The Public ◽  
Demand Chain ◽  
Customer Complaint ◽  
The Government

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

scholarly journals A News Big Data Analysis of Issues in Higher Education in Korea amid the COVID-19 Pandemic

2021 ◽  
Vol 13 (13) ◽  
pp. 7347
Author(s):  
Jangwan Ko ◽  
Seungsu Paek ◽  
Seoyoon Park ◽  
Jiwoo Park
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analysis ◽  
College Education ◽  
Big Data Analysis ◽  
Second Phase ◽  
Public Attention ◽  
Job Market ◽  
Media Reports ◽  
The Government

This paper examines the main issues regarding higher education in Korea—where college education experienced minimal interruptions—during the COVID-19 pandemic through a big data analysis of news articles. By analyzing policy responses from the government and colleges and examining prominent discourses on higher education, it provides a context for discussing the implications of COVID-19 on education policy and what the post-pandemic era would bring. To this end, we utilized BIgKinds, a big data research solution for news articles offered by the Korea Press Foundation, to select a total of 2636 media reports and conducted Topic Modelling based on LDA algorithms using NetMiner. The analyses are split into three distinct periods of COVID-19 spread in the country. Some notable topics from the first phase are remote class, tuition refund, returning Chinese international students, and normalization of college education. Preparations for the College Scholastic Ability Test (CSAT), contact and contactless classes, preparations for early admissions, and supporting job market candidates are extracted for the second phase. For the third phase, the extracted topics include CSAT and college-specific exams, quarantine on campus, social relations on campus, and support for job market candidates. The results confirmed widespread public attention to the relevant issues but also showed empirically that the measures taken by the government and college administrations to combat COVID-19 had limited visibility among media reports. It is important to note that timely and appropriate responses from the government and colleges have enabled continuation of higher education in some capacity during the pandemic. In addition to the media’s role in reporting issues of public interest, there is also a need for continued research and discussion on higher education amid COVID-19 to help effect actual results from various policy efforts.

From Data to Vision

2015 ◽  
pp. 1-14 ◽  
Author(s):  
Rhoda Joseph
Keyword(s):  
Big Data ◽  
Public Sector ◽  
Private Sector ◽  
Government Agency ◽  
Specific Context ◽  
Federal Level ◽  
The Public ◽  
Country Level ◽  
Current Survey ◽  
The Government

This chapter examines the use of big data in the public sector. The public sector pertains to government-related activities. The specific context in this chapter looks at the use of big data at the country level, also described as the federal level. Conceptually, data is processed through a “knowledge pyramid” where data is used to generate information, information generates knowledge, and knowledge begets wisdom. Using this theoretical backdrop, this chapter presents an extension of this model and proposes that the next stage in the pyramid is vision. Vision describes a future plan for the government agency or business, based on the current survey of the organization's environment. To develop these concepts, the use of big data is examined in three different countries. Both opportunities and challenges are outlined, with recommendations for the future. The concepts examined in this chapter are within the constraints of the public sector, but may also be applied to private sector initiatives pertaining to big data.

scholarly journals Big Data for a Fairer Democracy?

2016 ◽  
Vol 24 ◽  
Author(s):  
Jessica Heesen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Public Sphere ◽  
Freedom Of Expression ◽  
Big Data Analysis ◽  
Negative Freedom ◽  
The Public ◽  
Socially Relevant ◽  
The Public Sphere ◽  
Machine Communication

Big data-analysis is linked to the expectation to provide a general image of socially relevant topics and processes. Similar to this, the idea of the public sphere involves being representative of all citizens and of important topics and problems. This contribution, on one side, aims to explain how a normative concept of the public sphere could be infiltrated by big data. On the other, it will discuss how participative processes and common wealth can profit from a thorough use of big data analysis. As important parts of the argument, two concepts will be introduced: the numerical public (as a public that is constituted by machine-communication) and total politicisation (as a loss of negative freedom of expression).

From Data to Vision

Big Data ◽  
2016 ◽  
pp. 2149-2163
Author(s):  
Rhoda Joseph
Keyword(s):  
Big Data ◽  
Public Sector ◽  
Private Sector ◽  
Government Agency ◽  
Specific Context ◽  
Federal Level ◽  
The Public ◽  
Country Level ◽  
Current Survey ◽  
The Government

This chapter examines the use of big data in the public sector. The public sector pertains to government-related activities. The specific context in this chapter looks at the use of big data at the country level, also described as the federal level. Conceptually, data is processed through a “knowledge pyramid” where data is used to generate information, information generates knowledge, and knowledge begets wisdom. Using this theoretical backdrop, this chapter presents an extension of this model and proposes that the next stage in the pyramid is vision. Vision describes a future plan for the government agency or business, based on the current survey of the organization's environment. To develop these concepts, the use of big data is examined in three different countries. Both opportunities and challenges are outlined, with recommendations for the future. The concepts examined in this chapter are within the constraints of the public sector, but may also be applied to private sector initiatives pertaining to big data.

scholarly journals Oil Spill Response Policies to Bridge the Perception Gap between the Government and the Public: A Social Big Data Analysis

2020 ◽  
Vol 8 (5) ◽  
pp. 335 ◽  
Author(s):  
Joungyoon Chun ◽  
Jeong-Hwan Oh ◽  
Choong-Ki Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Oil Spills ◽  
Health And Safety ◽  
Big Data Analysis ◽  
Local Communities ◽  
The Public ◽  
Ecological Damage ◽  
Social Big Data ◽  
Local Residents

Oil spills cause socioeconomic and ecological damage to the marine environment and local communities. Implementing policies to effectively cope with such incidents is a challenging task due to the negative public perceptions about governmental responses. Using social big data, this study analyzed such negative perceptions in South Korea and the factors influencing them. The findings indicate that the public pays relatively little attention to oil spills but expresses serious concerns about the economic and ecological damage and the health and safety of volunteers and local residents. To improve public perception of oil spills, response strategies should aim to (1) analyze it using social big data to reduce the gap between governmental and public spheres, (2) release timely and accurate information to resolve civil distrust and dissatisfaction, (3) minimize direct damage to local communities and ecosystems affected by oil spills, and (4) reduce the impact on volunteers’ and local residents’ health and safety. Furthermore, through a multidisciplinary approach that combines social big data analysis methods with marine scientific research, it can contribute to creating a disaster response policy tailored to policy consumers.

scholarly journals Social Emotional Opinion Decision with Newly Coined Words and Emoticon Polarity of Social Networks Services

2019 ◽  
Vol 11 (8) ◽  
pp. 165 ◽  
Author(s):  
Jin Sol Yang ◽  
Myung-Sook Ko ◽  
Kwang Sik Chung
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Threshold Value ◽  
Big Data Analysis ◽  
Network Services ◽  
Social Big Data ◽  
The Social ◽  
Social Network Services ◽  
The Government ◽  
Social Opinions

Nowadays, based on mobile devices and internet, social network services (SNS) are common trends to everyone. Social opinions as public opinions are very important to the government, company, and a person. Analysis and decision of social polarity of SNS about social happenings, political issues and government policies, or commercial products is very critical to the government, company, and a person. Newly coined words and emoticons on SNS are created every day. Specifically, emoticons are made and sold by a person or companies. Newly coined words are mostly made and used by various kinds of communities. The SNS big data mainly consist of normal text with newly coined words and emoticons so that newly coined words and emoticons analysis is very important to understand the social and public opinions. Social big data is informally made and unstructured, and on social network services, many kinds of newly coined words and various emoticons are made anonymously and unintentionally by people and companies. In the analysis of social data, newly coined words and emoticons limit the guarantee the accuracy of analysis. The newly coined words implicitly contain the social opinions and trends of people. The emotional states of people significantly are expressed by emoticons. Although the newly coined words and emoticons are an important part of the social opinion analysis, they are excluded from the emotional dictionary and social big data analysis. In this research, newly coined words and emoticons are extracted from the raw Twitter’s twit messages and analyzed and included in a pre-built dictionary with the polarity and weight of the newly coined words and emoticons. The polarity and weight are calculated for emotional classification. The proposed emotional classification algorithm calculates the weight of polarity (positive or negative) and results in total polarity weight of social opinion. If the total polarity weight of social opinion is more than the pre-fixed threshold value, the twit message is decided as positive. If it is less than the pre-fixed threshold value, the twit message is decided as negative and the other values mean neutral opinion. The accuracy of the social big data analysis result is improved by quantifying and analyzing emoticons and newly coined words.

scholarly journals The influence of Airbnb on hotel occupancy in Mexico: a Big Data Analysis (2007-2018)

2021 ◽  
Vol 16 (1) ◽  
pp. 9-32
Author(s):  
Mario Gómez ◽  
Narciso Salvador Tinoco Guerrero ◽  
Luis Manuel Tinoco Guerrero
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Panel Data ◽  
Negative Impact ◽  
Public Information ◽  
Big Data Analysis ◽  
The Public ◽  
Negative Effect ◽  
Econometric Methodology ◽  
The One

The main objective of this paper is to analyze the influence that the usage of the Airbnb’s platform has had on hotel occupancy in Mexico during 2007- 2018 period. The Hotel Classification System is considered to know if there are differences in this influence, according to hotels’ category. To obtain the information from Airbnb, an application was created that extracted the public information of each lodging published on the website. Results were estimated by using the panel data econometric methodology, showing that the only negative impact the usage of Airbnb has on hotel occupancy is in 4-star hotels, and that an increase in the price of Airbnb’s lodgings produces a rise in hotel occupancy. In other hotel categories there is no negative effect. An implication is that the usage of platforms like the one studied can be moderately regulated in Mexico.

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