• Spatial Information Research
• /
• v.20 no.5
• /
• pp.99-109
• /
• 2012

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.5
• /
• pp.159-175
• /
• 2021

This study attempted to analyze the relationship between influencer's characteristic factors such as professionalism, authenticity, and interactivity and content quality factors consisting of accuracy, completeness, and diversity on brand attitude and purchase attitude through trust and self-consistency. To reveal the structural relationship between main variables, a survey was conducted on 201 users. An EFA, CFA, and reliability analysis were performed to confirm reliability and validity. And structural equation was conducted to verify hypothesis. The main results are as follows. First, it was found that professionalism and interactivity had a significant positive effect on trust. And, accuracy, completeness, and variety were all found to have a significant positive effect on trust. Second, in the relationship between individual characteristic factors and self-consistency, it was found that professionalism and authenticity had a significant positive effect on self-consistency. In addition, in the relationship between content quality and self-consistency, accuracy, completeness, and diversity were found to have a positive effect on self-consistency along with trust. Third, in the relationship between trust and self-consistency on brand attitude and purchase intention, both trust and self-consistency were found to have a statistically significant positive effect on brand attitude. It was found that only self-consistency and brand attitude had a statistically significant positive effect on purchase intention. These findings showed that when users perceive professionalism and interaction with influencer, trust increases, and professionalism and progress increase self-consistency with influencer. In addition, in the case of content quality, it was found that trust and self-consistency responded positively when perceived content quality through content accuracy, completeness, and diversity. Also, trust and self-consistency increased attitudes toward brands and could influence consumption behavior such as purchase intention. Therefore, for effective marketing performance using influencer's influence in the field of influencer marketing, which has a strong information delivery on products and brands, not only personal characteristics such as professionalism, authenticity, and interactivity, but also quality of content should be considered. The above research results are expected to suggest implications for marketing strategies and practices as one available basic data to exert the expected effect of marketing using influencer.

• Journal of the Korean Society for Library and Information Science
• /
• v.56 no.3
• /
• pp.47-72
• /
• 2022

This study aims to understand the trends of subjects related to real estate policies and public's emotional opinion on the policies. Two keywords related to real estate policies such as "real estate policy" and "real estate measure" were used to collect tweets created from February 25, 2008 to August 31, 2021. A total of 91,740 tweets were collected and we applied sentiment analysis and dynamic topic modeling to the final preprocessed and categorized data of 18,925 tweets. Sentiment analysis and dynamic topic model analysis were conducted for a total of 18,925 posts after preprocessing data and categorizing them into supply, real estate tax, interest rate, and population variance. Keywords of each category are as follows: the supply categories (rental housing, greenbelt, newlyweds, homeless, supply, reconstruction, sale), real estate tax categories (comprehensive real estate tax, acquisition tax, holding tax, multiple homeowners, speculation), interest rate categories (interest rate), and population variance categories (Sejong, new city). The results of the sentiment analysis showed that one person posted on average one or two positive tweets whereas in the case of negative and neutral tweets, one person posted two or three. In addition, we found that part of people have both positive as well as negative and neutral opinions towards real estate policies. As the results of dynamic topic modeling analysis, negative reactions to real estate speculative forces and unearned income were identified as major negative topics and as for positive topics, expectation on increasing supply of housing and benefits for homeless people who purchase houses were identified. Unlike previous studies, which focused on changes and evaluations of specific real estate policies, this study has academic significance in that it collected posts from Twitter, one of the social media platforms, used emotional analysis, dynamic topic modeling analysis, and identified potential topics and trends of real estate policy over time. The results of the study can help create new policies that take public opinion on real estate policies into consideration.