• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Korean Security Journal
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• no.62
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• pp.35-63
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• 2020

As the frequency and intensity of catastrophic disasters increase, there is widespread public sentiment that government capacity for disaster response and recovery is fundamentally limited, and that the involvement of civil society and the private sector is ever more vital. That is, in order to strengthen national disaster response capacity, governments need to build disaster systems that are more participatory and function through the channels of civil society, rather than continuing themselves to bear sole responsibility for these "wicked problems." With the advancement of smart mobile technology and social media, government and society as a whole have been called upon to apply these new information and communication technologies to address the current shortcomings of government-led disaster management. As illustrated in such catastrophic disasters as the 2011 Tohoku earthquake and tsunami in Japan, the 2010 Haitian earthquake, and Hurricane Katrina in the United States in 2005, the realization of participatory potential of smart technologies for better disaster response has enabled citizen participation via new smart technologies during disasters and resulted in positive impact on the management of such disasters. In this context, this study focuses on the South Korean context, and aims to analyze Korean government officials' readiness for public participation using smart technologies. On this basis, it aims to offer policy suggestions aimed at promoting smart technology-enabled citizen participation. For this purpose, it proposes a particular model, termed SMART (System, Motivation, Ability, Response, and Technology).

• Journal of the Society of Disaster Information
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• v.10 no.4
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• pp.518-527
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• 2014

Our society is changed and diversified rapidly and such tendency is accelerated day after day and has made a lot of problems in the many fields. The important thing we have to recognize is such tendency has a bad effect recently on the safety system in Korea. So it is time to enhance the national safety system and moreover recently Sewol-ho(passenger ship) went down in the sea, it made people remind the importance of national safety system. With this incident, Korean government decided to establish the national safety communication network against the disaster. At this time, I will propose several ideas about the national safety communication network. 1. It must to be established an unified network to contact people who is on a disaster site anytime and anywhere. This is most important element on all disaster sites. 2. PS-LTE technology must to be adopted to the network because it has many advantages including various multimedia services compared to the TETRA in the past. 3. 700MHz is the most efficient band for the network because it has wide cell sites coverage compared to 1.8GHz. 4. Satellite communication system is needed to the network for back-up. 5. It will be effective to adopt Social Media to the communication network system like a Twitter or Facebook for sharing many kinds of information and notifying people of warning message. 6. It can make the network more useful to introduce the latest technology like a sensor network. And Korean government has to improve the system related to the disaster including law and operating organization.

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

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Trans-
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• v.16
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• pp.1-31
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• 2024

The advent of digital media has facilitated easy access for adolescents to environments conducive to the purchase of narcotics. In particular, there's an increasing trend in the purchase and consumption of narcotics mediated through Social Network Services (SNS) and messenger services. Adolescents, sensitive to such environments, are at risk of experiencing neurological and mental health issues due to narcotic addiction, increasing their exposure to criminal activities, hence necessitating national-level management and support. Consequently, the quest for sustainable treatment methods for adolescents exposed to narcotics emerges as a critical challenge. In the context of high relapse rates in narcotic addiction, the necessity for cost-effective and user-friendly treatment programs is emphasized. This study conducts a literature review aimed at utilizing digital platforms to create an environment where adolescents can voluntarily participate, focusing on the development of therapeutic content through art. Specifically, it reviews societal perceptions and treatment statuses of adolescent drug addiction, analyzes the impact of narcotic addiction on adolescent brain activity and cognitive function degradation, and explores approaches for developing digital therapeutics to promote the rehabilitation of the addicted brain through analysis of precedential case studies. Moreover, the study investigates the benefits that the integration of digital therapeutic approaches and art therapy can provide in the treatment process and proposes the possibility of enhancing therapeutic effects through various treatment programs such as drama therapy, music therapy, and art therapy. The application of art therapy methods is anticipated to offer positive effects in terms of tool expansion, diversification of expression, data acquisition, and motivation. Through such approaches, an enhancement in the effectiveness of treatments for adolescent narcotic addiction is anticipated. Overall, this study undertakes foundational research for the development of digital therapeutics and related applications, offering economically viable and sustainable treatment options in consideration of the societal context of adolescent narcotic addiction.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.