Hyeoncheol Lee

Automated Twitter data collecting tool and case study with rule-based analysis

Applying data mining techniques to social media can yield interesting perspectives about individual human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to build your own data set to apply data mining techniques without an automated data gathering and filtering system because of main characteristics of social media: the data is large, noisy and dynamic. To overcome these challenges, we developed a java-based data gathering tool that continually collects social data from Twitter and filters noisy data. This allows us, as well as other researchers, to build our own Twitter database. In this paper, we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. In addition, we provide an analysis of Twitter messages about various Super Bowl ads by applying data-mining techniques to a case study.

Automated Twitter data collecting tool for data mining in social network

Applying data mining techniques to social media can yield interesting perspectives about individual human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to build your own data set to apply data mining techniques without an automated data gathering system. To overcome this challenge, we developed a java-based data gathering tool that continually collects social data from Twitter. This allows us, as well as other researchers, to build our own Twitter database. In this paper, we introduce the design specifications and explain the implementation details of the Twitter Data Collecting Tool we developed. In addition, we provide an in-depth analysis of Twitter messages about various Super Bowl ads by applying data-mining techniques to a case study. The study aims to address the question of how people use Twitter and to assess the power of Twitter in terms of creating consumer interest in brands and commercials.

Bridging Enterprise Architecture Requirements to ArchiMate

Properly aligned business and Information Technology (IT) can provide competitive edge to an organization. In order to align business with IT, IT should fully support business operations. Enterprise Architecture (EA) can be used to support business and IT alignment. To help design such systems using EA, Enterprise Architecture Frameworks (EAF) may be used. There are frameworks to support EA, such as Zachman Framework, the Department of Defense Architecture Framework (DoDAF), and The Open Group Architecture Framework (TOGAF). They help to design, evaluate, and build the right architecture and reduce the costs of planning, designing, and implementing [8]. ArchiMate is an open and independent architecture modeling language that complements EAF for modeling and visualizing EA. Regardless of chosen EAF, conversion processes from requirements to EA using ArchiMate are not well-defined to the best of our knowledge. Goal-oriented approach is a requirement analysis technique that supports early requirements analysis, which can be used to define a process for bridging EA requirements to ArchiMate. Therefore,we propose guidelines using goal-oriented approach and then apply them to the interoperability of prescriptions in a healthcare system.

Dynamic Seed Analysis in a Social Network for Maximizing Efficiency of Data Collection

Applying data mining techniques to social media can yield interesting perspectives to understanding individual and human behavior, detecting hot issues and topics, or discovering a group and community. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data sets: data is large, noisy, and dynamic. To collect the data related to a specific topic and keyword efficiently, we propose a new algorithm that selects the best seed nodes with limited resources and time. The algorithm also evaluates various user influence and activity factors, and updates the seed nodes dynamically during the gathering process. Furthermore, we compare two data sets collected by the algorithm and existing approaches.

Twitter data collecting tool with rule‐based filtering and analysis module

Purpose – It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.Design/methodology/approach – The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.Findings – The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study sh...

A real-time knowledge extracting system from social big data using distributed architecture

A huge amount of data is being generated by social media in real time. Accordingly, demands for extracting meaningful information from the social data have been dramatically increased. However, most of the previous research encompasses potential problems with data processing, management and analysis in real time. In this paper, we propose a distributed system architecture for generating meaningful information from text-based social data. The system collects data from multi-source channels, such as Twitter, YouTube, and The New York Times. Also, the system extracts terms and sentiment from each document using data mining technologies. In addition, the system uses HDFS, Map-reduce, and message service to handle the huge data. By analyzing keywords in texts and user account information, the system generates a summary of results including terms, sentiments and data variations for further analysis, including reputation, social trends, and customer reactions. The experiment results show that our approach is able to effectively process the social data in real time.

Documents topic classification model in social networks using classifiers voting system

Topic model uncovers abstract topics within texts documents, which is an essential task in text analysis in social networks. However, identifying topics in text documents in social networks is challenging since the texts are short, unlabeled, and unstructured. For this reason, we propose a topic classification system regarding the features of text documents in social networks. The proposed system is based on several machine-learning algorithms and voting system. The accuracy of the system has been tested using text documents that were classified into three topics. The experiment results show that the proposed system guarantees high accuracy rates in documents topic classification.

Efficient Keyword-Related Data Collection in a Social Network with Weighted Seed Selection

Data mining in a social can yield interesting perspectives to understanding human behavior or detecting topics or communities. However, it is difficult to gather the data related to a specific topic due to the main characteristics of social media data: large, noisy, and dynamic. To collect the data related to a specific topic efficiently, we propose a new algorithm that selects better seeds with limited resources. Furthermore, we compare two data sets collected by the algorithm and existing approaches.