• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.129-130
• /
• 2016

빅 데이터 분석은 기존 데이터베이스 관리 도구로부터 데이터를 수집, 저장, 관리, 분석할 수 있는 역량을 말한다. 대부분의 빅 데이터 분석 기술 방법들은 기존 통계학과 전산학에서 사용되던 데이터 마이닝, 기계 학습, 자연 언어 처리, 패턴 인식 등이 해당된다. 최근 스마트 기기의 발달과 정보통신기술의 발전은 트위터, 페이스북, 인스타그램 등의 소셜 네트워크상에서 유통되는 정보량이 폭발적 증가하고 있다. 이러한 변화는 데이터화가 가속화되고 있는 현대사회에서 데이터의 가치는 점점 높아질 것으로 예상되며, 데이터로부터 가치 있는 정보와 통찰력을 효과적으로 이끌어내는 기업이 경쟁력 확보를 위한 핵심가치가 되었다. 본 연구에서는 다음 커뮤니케이션의 빅 데이터 분석도구인 소셜 매트릭스를 활용하여 키워드 분석을 통해 스마트폰 플랫폼 키워드 의미를 분석하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.1579-1581
• /
• 2011

페이스북(Facebook)과 트위터(Twitter)등으로 각광 받는 소셜 네트워크 서비스(Social Network Service)는 사용자의 온라인과 오프라인에 구축된 인맥 네트워크를 기반으로 한다. SNS내의 소셜커머스, 소셜어플리케이션 등의 다양한 기능이 추가 되면서 새로운 온라인 서비스 시장이 등장하게 되었다. 초기의 새로운 가상 인맥 늘리기 수단으로 사용되었던 SNS서비스는 스마트폰의 등장과 카메라, 마이크 등의 추가 디바이스와 서비스간의 연계가 가능해 지면서 이용자들의 새로운 정보 생성과 실시간 커뮤니케이션이 가능해 졌다. 또한 SNS를 통한 정보의 흐름은 텍스트 기반의 한방향 정보 전달 틀에서 소리와 이미지, 동영상등의 다양한 미디어가 취합되는 공간이 됨으로서 사용자와 다른 사용자간의 상호 작용이 가능한 쌍방향 소통으로 현실의 정보를 보다 정확하고 빠르게 전달할 수 있는 하나의 미디어 형태로 진화하는 중이다. 본 논문에서는 소셜 서비스에서 생성된 정보가 집단 협업(Mass Collaboration)을 이룰 때 갖는 신뢰성을 기대하여 키워드 중심의 정보 형성에 따른 SNS 활용 방법을 제시한다.

• Journal of the Korean Society for Library and Information Science
• /
• v.55 no.4
• /
• pp.5-22
• /
• 2021

This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.24 no.4
• /
• pp.75-90
• /
• 2013

Given the context that users are actively using social media with multimedia embedded information, the purpose of this study is to demonstrate how images are used within Twitter messages, especially in influential and favorited messages. In order to achieve the purpose of this study, the top 200 influential and favorited messages with images were selected out of 1,589 tweets related to "Boston bombing" in April 2013. The characteristics of the message, image use, and user are analyzed and compared. Two phases of the analysis were conducted on three data sets containing the top 200 influential messages, top 200 favorited messages, and general messages. In the first phase, coding schemes have been developed for conducting three categorical analyses: (1) categorization of tweets, (2) categorization of image use, and (3) categorization of users. The three data sets were then coded using the coding schemes. In the second phase, comparison analyses were conducted among influential, favorited, and general tweets in terms of tweet type, image use, and user. While messages expressing opinion were found to be most favorited, the messages that shared information were recognized as most influential to users. On the other hand, as only four image uses - information dissemination, illustration, emotive/persuasive, and information processing - were found in this data set, the primary image use is likely to be data-driven rather than object-driven. From the perspective of users, the user types such as government, celebrity, and photo-sharing sites were found to be favorited and influential. An improved understanding of how users' image needs, in the context of social media, contribute to the body of knowledge of image needs. This study will also provide valuable insight into practical designs and implications of image retrieval systems or services.

• Journal of the Korean association of regional geographers
• /
• v.23 no.2
• /
• pp.376-387
• /
• 2017

This paper attempts to analyze the geographical characters of Twitter data and presents analysis potentials for social network analysis in geography. First, this paper suggests a methodology for a topic modeling-based approach in order to identify the geographical characteristics of tweets, including an analysis flow of Twitter data sets, tweet data collection and conversion, textural pre-processing and structural analysis, topic discovery, and interpretation of tweets' topics. GPS coordinates referencing tweets(geotweets) were extracted among sampled Twitter data sets because it contains the tweet place where it was created. This paper identifies a correlated relationship between some specific topics and local places in Jeju. This correlation is closely associated with some place names and local sites in Jeju Island. We assume it is the intention of tweeters to record their tweet places and to share and retweet with other tweeters in some cases. A surface density map shows the hotspots of tweets, detecting around some specific places and sites such as Jeju airport, sightseeing sites, and local places in Jeju Island. The hotspots show similar patterns of the floating population of Jeju, especially the thirty-year age group. In addition, a topic modeling algorithm is applied for the geographical topic discovery and comparison of the spatial patterns of tweets. Finally, this empirical analysis presents that Twitter data, as social network data, provide geographical significance, with topic modeling approach being useful in analyzing the textural features reflecting the geographical characteristics in large data sets of tweets.

• Journal of Intelligence and Information Systems
• /
• v.18 no.2
• /
• pp.85-105
• /
• 2012

In recent years, IT advancement has brought out the new Internet communication environment such as online social network services, where people are connected in global network without temporal and spatial limitation. The popular use of online social network helps people share their experience and preference for specific products and services, thus holding large potential to significantly affect firms' business performance through Word-of-Mouth (WOM). This study examines the role of online social network in raising WOM effect on the movie industry by comparing with the similar role of Internet portal, another major online communication channel. Analyzing 109 movies and data from both Twitter and Naver movie, we found that significant WOM effect exists simultaneously in both Twitter and Naver movie. However, we also found that different figures of online viral effects exist depending on the popularity of movies. In the hit movie group, before the movie release, the WOM effect occurs only in Twitter while the WOM effect arises in both Twitter and Naver movie at the same time after the movie release. In the less-popular (or niche) movie group, the WOM effect occurs in both Twitter and Naver movie only before the movie release. Our findings not only deepen theoretical insights into different roles of the two online communication channels in provoking the WOM effect on entertainment products but also provide practitioners with incentive to utilize SNS as strategic marketing platform to enhance their brand reputations.

• Information Systems Review
• /
• v.21 no.1
• /
• pp.91-111
• /
• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• Journal of the Korean Society for information Management
• /
• v.36 no.4
• /
• pp.207-226
• /
• 2019

Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their mental states. In this study, large amounts of Korean text were collected and analyzed to determine whether such data could be used to detect depression in users. This study analyzed data collected from Twitter users who had and did not have depressive tendencies between January 2016 and February 2019. The data for each user was separately analyzed before and after the appearance of depressive tendencies to see how their expression changed. In this study the data were analyzed through co-occurrence word analysis, topic modeling, and sentiment analysis. This study's automated data collection method enabled analyses of data collected over a relatively long period of time. Also it compared the textual characteristics of users with depressive tendencies to those without depressive tendencies.

• Journal of KIISE
• /
• v.44 no.12
• /
• pp.1258-1268
• /
• 2017

Capturing real-time daily information on food prices is invaluable to help policymakers and development organizations address food security problems and improve public welfare. This study analyses the possible use of large-scale online data, available due to growing Internet connectivity in developing countries, to provide updates on food security landscape. We conduct a case study of Indonesia to develop a time-series prediction model that nowcasts daily food prices for four types of food commodities that are essential in the region: beef, chicken, onion and chilli. By using Twitter price quotes, we demonstrate the capability of social data to function as an affordable and efficient proxy for traditional offline price statistics.

• Journal of Digital Convergence
• /
• v.11 no.6
• /
• pp.129-135
• /
• 2013

With the increasing use of the SNS, WOM(Word of Mouth) has become an even more powerful and useful resource for consumers and marketers. In this paper, we investigated the effects of message polarity (positive, neutral, negative) and type (factual, evaluative) on WOM through SNS (twitter). A total of 13.4 million twitter messages were collected and 1.0 million retweeted messages were analyzed. The results showed that message orientation, type, URL and hashtag have a significant (<0.01) effect on retweet counts and the interaction between message orientation and other factors were observed. It also observed that message type, URL and hashtag have significant (0.05) relationships with retweet speed.