scholarly journals Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures

2021 ◽  
Author(s):  
Fei Shen ◽  
Chuanli Xia ◽  
Wenting Yu ◽  
Chen Min ◽  
Tianjiao Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Hong Kong ◽  
Public Opinion ◽  
Social Science ◽  
The Internet ◽  
Research Grants ◽  
Mining Project ◽  
Media And Communication ◽  
Communication Methods

The Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) aims to collect and study online public opinion in Hong Kong. Funded by the Research Grants Council of Hong Kong (Project No. 11600717), this project started in January 2018 and is currently housed in the Department of Media and Communication at City University of Hong Kong. The project team is led by the principal investigator Dr. Fei Shen and comprised of scholars and researchers with specializations in communication, computer science, and other social science domains. Utilizing a big data approach, the current project uses a series of computational communication methods to extract and analyze the dynamics of public opinion on the internet in Hong Kong.

Big Data in manufacturing systems engineering – close up on a machine tool

2016 ◽  
Vol 64 (7) ◽  
Author(s):  
Christian Bauer ◽  
Zaigham-Faraz Siddiqui ◽  
Manuel Beuttler ◽  
Klaus Bauer
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
Systems Engineering ◽  
Machine Tools ◽  
Manufacturing Systems ◽  
The Internet ◽  
New Approach ◽  
New Challenges ◽  
The Internet Of Things

AbstractWith the increasing connectivity of devices, the amount of data that is recorded and ready for analysis is growing correspondingly. This is also the case for shop floors in flexible sheet metal handling and production. With the growing need for flexibility in production, the availability of machine tools is imminent. This paper shows different approaches that a classical manufacturing systems company such as TRUMPF takes in applying data mining techniques to address the new challenges which come with the Internet of things. In addition to classical methods, a new approach is introduced that does not need any alteration of the machine or its interfaces.

scholarly journals Recommender System using Content Based Filtering for News Portal in Indonesia

2020 ◽  
Vol 8 (6) ◽  
pp. 173-178
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender System ◽  
Information Overload ◽  
Cosine Similarity ◽  
News Article ◽  
The Internet ◽  
New Model ◽  
Digital Era ◽  
Content Based Filtering

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.

scholarly journals Research on Mining and Application of Group Events Based on Network Public Opinion Big Data

2021 ◽  
Author(s):  
Weimin Gao ◽  
Jiaming Zhong ◽  
Yuan Xiao
Keyword(s):  
Big Data ◽  
Public Opinion ◽  
Social Life ◽  
The Internet ◽  
Sir Epidemic Model ◽  
Control Effect ◽  
Public Events ◽  
Sir Epidemic ◽  
The Government ◽  
Network Public Opinion

Network Public Opinion is significant in maintaining social harmony and stability and promoting transparency in government affairs. However, with the development of economy and transformation of society, our country has entered a high-risk period, which is full of unexpected public events. Unexpected mass accidents also cause hot discussions among the Internet users once they are exposed on the network. Different ideas, opinions, emotions, and attitudes about unexpected public events will be collected and collide on the Internet. It makes Network Public Opinion play an increasingly important role in the evolution of unexpected public events. It could promote the spread and upgrade of unexpected public events and bring more and more profound influence on to our social life. We use the case study method to analyze and solve the problems by applying the dynamic principles of the SIR epidemic model, comprehensively considering the social environment and various influencing factors, and constructing a mathematical model for the spread of network group events. The study uses Matlab to simulate the change trajectory of the number of participants in the network group events. By adjusting the number of contacts φ in the model, the development of network group emergencies can be effectively controlled and managed. As long as the government takes timely intervention measures, the dissemination of network group events can be basically controlled. Combined with public opinion big data to discover the important factors affecting the spread of public opinion, the control effect is obvious.

scholarly journals An Optimal Enhancement of the Dynamic Features of Recommender Systems

2019 ◽  
Vol 8 (2S4) ◽  
pp. 51-55
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Recommender Systems ◽  
Data Analytics ◽  
Recommendation Systems ◽  
Big Data Analytics ◽  
The Internet ◽  
User Requirements ◽  
Dynamic Features ◽  
Dynamic Nature

Recommendation systems come under the domain of Data mining and Big Data analytics. It is useful tool that is used to predict the ratings or preferences of a user from a pool of resources. The preferences of user are dynamic in nature. The immeasurable usage of internet is having a great impact on the way we deal our lives and communicate with each other. As a result, the requirements of user browsing the internet are changing radically. Recommender Systems (RSs) provide a technology that helps users in finding relevant or preferential information among the pool of information using internet. This paper puts forward not only the issues related to the dynamic nature of user’ requirements but also the changes in the systems’ contents. The Recommendation Systems which involves the above stated issues are termed as Dynamic Recommender Systems (DRSs). This paper first defines the concept of DRS and then explores the various parameters that is taken into account in developing a DRS. This paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Research on Key Technology of Internet Public Sentiment

2014 ◽  
Vol 926-930 ◽  
pp. 2030-2033
Author(s):  
De Zhi An ◽  
Yun Ke
Keyword(s):  
Public Opinion ◽  
Social Science ◽  
Natural Science ◽  
Hot Spot ◽  
The Internet ◽  
Public Opinion Research ◽  
Key Technology ◽  
The Public ◽  
Public Sentiment ◽  
The Status

Public opinion research is a new internet discipline of social science and natural science. As a hot spot of public opinion research, the research on the public opinion on the Internet has attracted much attention. By analyzing the status of the research on the public opinion on the Internet in China, this paper establishes the basic framework of the research on the public opinion on the Internet. Then some key technology issues are researched in detail. Based on the method and key technology, the paper introduces the design and implement about the platform of Internet Public Sentiment. This paper is expected to have the value to apply the Internet public sentiment analysis.

Analysising on the Library Services in the Age of Big Data

2014 ◽  
Vol 1044-1045 ◽  
pp. 1066-1070
Author(s):  
Chen Wei ◽  
Xiao Di Wang ◽  
Ran Ma ◽  
Bing Qi Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Social Networking ◽  
Data Storage ◽  
Rapid Development ◽  
Structured Data ◽  
The Internet ◽  
Library Services ◽  
Library Service

The advent of the age of big data brings not only the rapid development of the Internet, scientific research, social networking and other fields, but also help and challenges to the application of library. For example, the library service applications in data storage, data mining, data analysis, etc. can identify hidden values behind the data only through systematic organization and analysis of massive structured, unstructured, and semi-structured data, ​​in order to predict the future development of library and promote its better development.

scholarly journals The Growth of Negative Sentiment in Post-Umbrella Movement Hong Kong: Analyzing Public Opinion Online from 2017 to 2019

2021 ◽  
Author(s):  
Fei Shen ◽  
Chuanli Xia ◽  
Wenting Yu ◽  
Chen Min ◽  
Tianjiao Wang ◽  
...  
Keyword(s):  
Hong Kong ◽  
Public Opinion ◽  
News Media ◽  
Social Issues ◽  
Online News ◽  
Time Span ◽  
Discussion Forums ◽  
Public Figures ◽  
Mining Project ◽  
Negative Sentiment

This report presents a part of the findings from the Hong Kong Online Public Opinion Data Mining Project (http://www.webopinion.hk/) that aims to collect and analyze online public opinion towards different issues and topics in Hong Kong. The report provides an overview of public opinion on a variety of topics such as public figures, organizations, and social issues. A total of 12 online platforms including discussion forums, news portal sites, and online news media are included in the analysis (for methodological details, see Text-Mining Online Public Opinion in Hong Kong: Methods and Procedures https://osf.io/preprints/socarxiv/b2mex/). The time span of the analysis in this report is from July 2017 to December 2019.

Sign in / Sign up

Export Citation Format

Share Document