• Journal of Korea Multimedia Society
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• v.17 no.11
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• pp.1286-1295
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• 2014

This paper suggests the way that it could improve the reliability about preference of user's feedback by adding weighting factor on sentiment analysis, and efficiently make a sentiment analysis of users' emotional perspective on the big data massively generated on twitter. To solve errors on earlier studies, this paper has improved recall and precision of sensibility determination by using sensibility dictionary subdivided sentiment polarity based on the level of sensibility and given impotance to sensibility determination by populating slang, new words, emoticons and idiomatic expressions not in the system dictionary. It has considered the context through conjunctive adverbs fixed in korean characteristics which are free to the word order. It also recognize sensibility words such as TF(Term Frequency), RT(Retweet), Follower which are weighting factors of preference and has increased reliability of preference analysis considering weight on 'a very emotional tweet', 'a recognised tweet from users' and 'a tweeter influencer'

• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.241-262
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• 2017

This study aims to identify and analyze the Twitter use of domestic public libraries. In order to identify the detailed patterns of Twitter use in library and information services, a content analysis was conducted for the 3,038 tweet data from the top 14 public libraries' accounts on Twitter use. Inductive approach was adopted to develop a coding scheme and open coding was conducted with the entire tweet. Additionally, correspondence analysis was conducted for the result of content analysis to identify how library accounts correspond to specific types. As a result, 3 main categories and 9 sub-categories of public libraries' Twitter use were developed. And the 37 detailed patterns of public libraries' use of Twitter were identified. The identified patterns can provide the libraries interested in Twitter use with guidelines.

• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.131-140
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• 2013

Assuming that roles of traditional mass media are also shown in Twitter services, the study aims at exploring Twitter users' motives and rationales in re-tweet behavior. Based on the laddering interview method, the study gathers data on (1) message attributes (what kinds of messages do you re-tweet?); (2) consequences (what kinds of consequences are you expecting when you re-tweet?); and (3) values (what are the ultimate values in your re-tweet behavior?). The most repetitive value occurring in participants' retweet was feeling "sympathy" and "sharing" rationales. For such rationales, participants oftentimes utilize messages with "agenda" and "information" that are relative to themselves. Messages with "helping" to help others also frequently showed up in their retweet rationales. Known as liberalists' rationales, "communal consciousness", and "calling for others' action" are also shown, but not as frequent as "feeling sympathy and sharing. A total of 48 items from the analyses were used in a subsequent study as variables to identify factors (dimensions) of retweet motivation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.252-255
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• 2011

Growing demand for smart phones and data communication diminishes the constraints of Twitter and Facebook than a smartphone has become a subject of interest. On the other hand facebook users in their relationships to obtain the consent of the other, twitter is a relatively simple procedure for the information ripple effect is excellent. Twitter is beyond a simple social networking services(SNS) located in one of the popular media and powerful have the upper retweet. Retweet to the top of his sympathy with the ability th send tweets to their subscriber information can spread quickly. In this paper, we propose the smart retweet that system actively extend the existing retweet. In order to realize the smart retweet and additional criteria for determining the destination of the information is required. Based on tweet generated regional or an local information mentioned to tweet, to determine the destination. Smart retweet of the speed and scope of information transmission through the scale is expected.

• Spatial Information Research
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• v.22 no.3
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• pp.1-8
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• 2014

While SNS services such as Twitter or Facebook are rapidly growing, research for the SNS analysis has been concerned. Especially, twitter reacts to social issues in real-time so that it is used to get useful experimental data for researchers of social science or information retrieval. However, it is still lack of research on the methodology to collect data. Therefore, this paper suggests the tweet acquisition system by considering tendency of twitter user oriented location-based event and political social event. First the system acquires tweets including information of location and keyword about event and secure IDs for acquisition of political social event. Then we plan ID-analyzer to classify the tendency of users. In addition for measuring reliability of ID-analyzer, it acquires and analyzes the tweet by using high-ranked ID. In analyses result, top-ranked ID shows 88.8% reliability, 2nd-ranked ID shows 76.05% and ID-analyzer shows 77.5%, it shortens collection time by using minority ID.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.55-62
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• 2016

Economic and social damages are expected due to yellow dust, occurring every year in Korea and risk of citizens is getting higher accordingly. This study acquired tweet data for yellow dust, which had been the greatest since 2009 for 11 days before and after February 23, 2015. After that, it conducted an analysis on the issue words and association rule. Regarding acquired tweet data, the results of analyzing issue words by using open source R, statistics language shows that 'Mask' was ranked to be the highest, followed by health-related issue words. This indicates that people put the priority in the use of mask for keeping their health, as a result of the occurrence of yellow dust, and subsequently, they showed an interest in diseases, caused by yellow dust. In addition, yellow dust-related diseases, 'cold', 'rhinitis', 'flu', 'asthma', 'bronchitis' were found as issue words, revealing that people had a high concern on the disease occurrence of the respiratory system. The analytical results are judged to reflect the citizen's thought effectively in the process of establishing measures for the prevention of yellow dust.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.506-509
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• 2017

Opinion mining is a way to analyze the emotions in the text and is used to identify the emotional state of the author and to find out the opinions of the public. As you can analyze individual emotions through opinion mining, if you analyze the text by region, you can find out the emotional state you have in each region. The regional sentiment analysis can obtain information that could not be obtained from personal sentiment analysis, and if a certain area has emotions, it can understand the cause. For regional sentiment analysis, we need text data created by region, so we need to collect data through Twitter crawling. Therefore, this paper designs a Twitter data collection system for regional sentiment analysis. The client requests the tweet data of the specific region and time, and the server collects and transmits the requested tweet data from the client. Through the latitude and longitude values of the region, it collects the tweet data of the area, and it can manage the text by region and time through collected data. We expect efficient data collection and management for emotional analysis through the design of this system.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Journal of KIISE
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• v.41 no.12
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• pp.1136-1144
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• 2014

Twitter is a social network service that generates about 140 million contents a day. Contents of Twitter contain a variety of information and many researchers research those in various fields. In this research, we propose a method for evaluating the importance of content based on characteristics of Twitter. We have found that number of follower means user's popularity and Re-tweet that means the popularity of content. We perform experiments about proposed method using real Twitter data for proving effectiveness of proposed method. Also, we found information providers in Twitter are public user who represent a company or a representative of a specific group.

• 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.