• Annual Conference on Human and Language Technology
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• 2012.10a
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• pp.160-162
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• 2012

문서를 대상으로 한 다양한 감정 분류 연구가 진행되어 왔으며, 최근에는 트윗 감정 분류에 그대로 적용되고 있다. 그러나 트윗은 일반 문서와 다르게 몇 가지의 독특한 특징을 갖고 있어 좋은 성능을 보이지 못하고 있다. 본 논문에서는 기계학습을 기반으로 트윗의 특징과 트윗 사용자 정보 자질을 사용한 실험으로 트윗 감정 분류 성능의 영향을 확인하였다. 실험 결과 트윗에 포함된 이모티콘 감정 극성과, 사용자 성향 극성 자질은 트윗 감정 분류 모델의 성능 향상에 기여를 하는 것을 알 수 있었다.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.179-180
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• 2019

Nowadays, clustering algorithm is considered as a promising solution for lacking human-labeled and massive data of social media sites in numerous machine learning tasks. Many researchers propose disaster event detection systems have ability to determine special local events, such as missing people, public transport damage by clustering similar tweets and hashtags together. In this paper, we try to extend tweet hashtag feature definition by applying word embedding. The experimental results are described that word embedding achieve better performance than the reference method.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1145-1146
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• 2013

인터넷과 스마트폰의 발달로 SNS서비스의 사용자와 데이터가 활발하게 증가하고 있다. 이로 인하여 SNS 데이터의 가치와 신뢰성이 점점 증가하고 있으며, 이러한 추세에 따라 여러 연구와 실험을 통하여 데이터를 분석하고 분석 결과를 제공하는 서비스가 증가하고 있다. 본 논문에서는 이러한 배경을 바탕으로 특정 키워드를 포함하고 있는 한글 트윗을 검색하여 해당 트윗에 대한 연관 키워드와 감정 키워드를 분석해서 출력해주는 시스템을 개발한다.

• Annual Conference of KIPS
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• 2010.11a
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• pp.1327-1330
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• 2010

최근 전세계적으로 마이크로-블로그 형태의 소셜네트워크 서비스가 확산되어가고 있으며, 트위터(Twitter)란 이러한 가장 대표적인 소셜네트워크 서비스이다. 본 논문에서는 트위터를 매개로써 이루어지는 악성코드 유포 행위를 조사하기 위해 트위터에 올라오는 게시물(Tweet)들에서 약 93 만개의 링크를 임의 추출하여 다운받았고, 이중 7 개의 악성코드 배포 계정을 검출하여 해당 게시물과 계정의 특징을 조사하였다.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.263-285
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• 2016

Nowadays, Social Network Service (SNS), which has been in the spotlight as a way of communication, has become a most effective tool to improve easy of information use and accessibility for users. In this paper, we chose Twitter as the most representative SNS services because of automatic crawling and investigated tweet data gathered from domestic and foreign National Archives - NARA of U.S.A., TNA of U.K.. NAA of Australia, and National Archives of Korea. We also conducted information genres analysis and trend analysis by timeline. Information genres analysis shows how archives satisfied users' information needs as well as trends analysis of tweets helps to understand how users' interestedness was changed. Based on comparison results, we distilled four characteristics of National Archives and suggested vitalization ways for National Archives of Korea.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.43-51
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• 2014

Social Network Service has the sufficient potential can be widely and effectively used for various fields of society because of convenient accessibility and definite user opinion. Above all Twitter has characteristics of simple and open network formation between users and remarkable real-time diffusion. However, real analysis is accompanied by many difficulties because of semantic analysis in 140-characters, the limitation of Korea natural language processing and the technical problem of Twitter is own restriction. This thesis paid its attention to human's political attitudes showing permanence and assumed that if applying it to the analytic design, it would contribute to the increase of precision and showed it through the experiment. As a result of experiment with Tweet corpus gathered during the election of national assemblymen on 11st April 2012, it could be known to be considerably similar compared to actual election result. The precision of 75.4% and recall of 34.8% was shown in case of individual Tweet analysis. On the other hand, the performance improvement of approximately 8% and 5% was shown in by-timeline political attitude analysis of user.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.91-94
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• 2011

The LBSNS(Location-Based Social Networking Service) service has been well-received by researchers and end-users, such as Twitter. Location-Based service of Twitter is now structured that users could not subscribe the information of their interesting local area. Those who being following from someone tweet message included information of local area to them just for their own interesting. However, follower may receive that kind of tweet. In order to handle the problem, we propose filtering technique using spatial join. The first work for filtering technique is to add a location information to tweets and users. In this paper, location information is represented by MBR(Minimum Bounding Rectangle). Location information is divided into dynamic property and static property. Suppose that users are continuously moving, that means one of the dynamic property's example. At this time, a massive continous query could cause the problem in server. In this paper, we create Virtual Grid on Google Map for reducing frequency of query, and conclude that it is useful for server.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.81-91
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• 2019

Recently, social network service (SNS) is actively used by public. Among them, Twitter has a lot of tweets including sentiment and it is convenient to collect data through open Aplication Programming Interface (API). In this paper, we analyze social issues and suggest the possibility of using them in marketing through sentimental information of users. In this paper, we collect twitter text about social issues and classify as positive or negative by sentiment classifier to provide qualitative analysis. We provide a quantitative analysis by analyzing the correlation between the number of like and retweet of each tweet. As a result of the qualitative analysis, we suggest solutions to attract the interest of the public or consumers. As a result of the quantitative analysis, we conclude that the positive tweet should be brief to attract the users' attention on the Twitter. As future work, we will continue to analyze various social issues.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.39-45
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• 2018

Twitter users use 'Following', 'Retweet' and so on to find tweets that they are interested in. However, it is difficult for users to find tweets that are of interest to them on Twitter, which has more than 300 million users. In this paper, we developed a customized tweet recommendation system to resolve it. First, we gather current trends to collect tweets that are worth recommending to users and popular tweets that talk about trends. Later, to analyze users and recommend customized tweets, the users' tweets and the collected tweets are categorized. Finally, using Web service, we recommend tweets that match with user categorization and users whose interests match. Consequentially, we recommended 67.2% of proper tweet.