• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.115-133
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• 2015

This study investigates the contents of twitters serviced by metropolitans and citizens' questions to propose improvements. Using content analysis as a research method, this study recorded and analyzed all the tweets of six metropolitans (Seoul, Busan, Daegu, Incheon, Daejeon, Gwangju) for three months. As the results, the frequency analysis of tweets revealed that Busan posted more tweets than other cities, and Seoul posted the highest number of tweet using URL link. The results of content analysis showed that the most frequently provided information from tweeters was about convenience of citizens living. Tweets using URL link were focused on information about citizen living, prize contest, and service announcement. Citizens had a request for information about their life and traffic. For public information service using tweeter in the future, this study provided several important suggestions.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.667-677
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• 2013

With the huge success of Social Network Services, studies on social network analysis to extract the current issues or to track the symptoms of epidemic disease are being carried out actively. On Twitter, tweets reflect people's reaction to an event and users' individual status well, so it is possible to detect an event regarding a tweet as a sensory value. Recently, social signals are used to detect the spread of illness like the flu as well as the occurrence of disaster event like an earthquake in early stages. In this paper, we set up a cold as a target event and regarded tweets as Cold Signals. To evaluate the reliability of Cold Signals, we analyzed correlations between weather factors and the cold index provided by Korea Meteorological Administration.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.65-75
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• 2014

The main focus of this paper is to analyze trend of event informations in a variety of mass media by graphical visualization in axis of the time and location. Especially, continuity analysis based on user-generated social media can reflect the social impact of a certain event according to change time and location and their directional changes. To reveal the characteristics of continuous events, we survey the data set collected from news articles and tweets during two years. Based on case studies on 'disease' and 'leisure', we verify the effectiveness and usefulness of our proposed method. Even though some events occurred during same period, we showed directional changes which have high-impact in social media referred user interest's, compared with fact-based continuous visualization results.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1167-1173
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• 2015

The development of information and communication technology like SNS, blogs, and bulletin boards, was provided a variety of places where you can express your thoughts and comments and allowing Big Data to grow, many people reveal the opinion of the social issues in SNS such as Twitter. In this study, we would like to pre-built sentimental dictionary about social issues and conduct a sentimental analysis with structured dictionary, to gather opinions on social issues that are created on twitter. The data that I used is "bikini", "nakkomsu" including tweet. As the result of analysis, precision is 61% and F1- score is 74%. This study expect to suggest the standard of dictionary construction allowing you to classify positive/negative opinion on specific social issues.

• International Journal of Contents
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• v.15 no.4
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• pp.65-73
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• 2019

Social media is a massive dataset in which individuals' thoughts are freely recorded. So there have been a variety of efforts to analyze it and to understand the social phenomenon. In this study, Twitter was used to define the moments when negative perceptions of the Korean Meteorological Administration (KMA) were displayed and the reasons people were dissatisfied with the KMA. Machine learning methods were used for sentiment analysis to automatically train the implied awareness on Twitter which mentioned the KMA July-October 2011-2014. The trained models were used to validate sentiments on Twitter 2015-2016, and the frequency of negative sentiments was compared with the satisfaction of forecast users. It was found that the frequency of the negative sentiments increased before satisfaction decreased sharply. And the tweet keywords and the news headlines were qualitatively compared to analyze the cause of negative sentiments. As a result, it was revealed that the individual caused the increase in the monthly negative sentiments increase in 2016. This study represents the value of sentiment analysis that can complement user satisfaction surveys. Also, combining Twitter and news headlines provided the idea of analyzing the causes of dissatisfaction that are difficult to identify with only satisfaction surveys. The results contribute to improving user satisfaction with weather services by efficiently managing changes in satisfaction.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.4
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• pp.177-184
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• 2017

The abstract should concisely state what was done, how it was done, principal results, and their significance. It should be less than 300 words for all forms of publication. Recently social disasters have been occurring frequently in the increasing complicated social structure, and the scale of damage has also become larger. Accordingly, there is a need for a way to prevent further damage by rapidly responding to social disasters. Twitter is attracting attention as a countermeasure against disasters because of immediacy and expandability. Especially, collecting public opinion on Twitter can be used as a useful tool to prevent disasters by quickly responding. This study proposes a collecting method of Twitter public opinion through keyword analysis, issue topic tweet detection, and time trend analysis. Furthermore we also show the feasibility by selecting the case of humidifier disinfectant which is a social issue recently.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.141-155
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• 2017

As the use of SNS becomes more active, many people are posting their thoughts about specific events in their SNS in the form of text. As a result, SNS is used in various fields such as finance and distribution to conduct service satisfaction surveys and consumer monitoring. However, in the transportation area, there are not enough cases to utilize unstructured data analysis such as emotional analysis. In this study, we developed an emotional analysis methodology that can be used in transportation by using highway VOC data, which is atypical data collected by Korea Expressway Corporation. The developed methodology consists of morpheme analysis, emotional dictionary construction, and emotional discrimination of the collected unstructured data. The developed methodology was verified using highway related tweet data. As a result of the analysis, it can be guessed that many information and information about the construction and the accident were related to the highway during the analysis period. Also, it seems that users complain about the delay caused by construction and accident.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.21-30
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• 2016

Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.