• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.123-131
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• 2020

The text ranking algorithm is a representative method for keyword extraction, and its importance is emphasized highly. In this paper, we compare the performance of recent research and experiments with TF-IDF, SMART, INQUERY and CCA algorithms, which are used in text ranking algorithm.. After explaining each algorithm, we compare the performance of each algorithm based on the data collected from news and Twitter. Experimental results show that all of four algorithms can extract specific words from news data equally. However, in the case of Twitter, CCA has the best performance to extract specific words, and INQUERY shows the worst performance. We also analyze the accuracy of the algorithm through six comparison metrics. The experimental results present that CCA shows the best accuracy in the news data. In case of Twitter, TF-IDF and CCA show similar performance and demonstrate good performance.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.153-158
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• 2016

As the number of social network users increases, the information on event such as social issues and disasters receiving attention in each region is promptly posted by the bucket through social media site in real time, and its social ripple effect becomes huge. This study proposes a detection method of events that draw attention from users in specific region at specific time by using twitter data with regional information. In order to collect Twitter data, we use Twitter Streaming API. After collecting data, We implemented event detection system by analyze the frequency of a keyword which contained in a twit in a particular time and clustering the keywords that describes same event by exploiting keywords' co-occurrence graph. Finally, we evaluates the validity of our method through experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.905-907
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• 2014

Twitter is the special place that people in the world can freely share their information and opinion. There are tries to utilize a vast amount of information made from twitter. The study on classification of tweets by subject is actively conducted. Twitter is a service for sharing information with short 140-characters text message. The short message including brief content makes extracting a variety of information hard. In the paper, we suggests the method to classify tweet by subject. The method uses both tweet and subject features. In order to conduct experiments to verify the proposed method, we collected 10,000 tweet messages with the Twitter API. Through the experimental results, we will show that the performance of our proposed method is better than those of previous methods.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.471-483
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• 2014

The purpose of this study was to look over the sharing and flow of information on tourism tourist destination made through social media by utilizing social network analysis. In this research for compared the exchange and diffusion relationships and made information sharing among users of the network group for the Seoul / Busan Tourism Twitter. The first part of analysis dealt with the network centralization and information dissemination patterns from a global perspective. The second part was an analysis of nodes with the highest degree centrality in order to identify the most influential user within the Twitter network and determine the information flow and user characteristics with respect to the information dissemination pattern in Seoul/Busan tourism Twitter. The results scientific evidence was presented that social media users' information search behavior information to understand the sharing and spreading patterns, social media as a marketing tool, simple products and services that will ultimately provide the desired information detainee information is not a means of promoting utilization was presented.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.301-308
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• 2015

SNS is a web-based online platform service supporting the formation of relations between users. SNS users have usually used a desktop or laptop for this purpose so far. However, the number of SNS users is greatly increasing and their access to the web is improving with the spread of smart phones. They share their daily lives with other users through SNSs. We can detect events if we analyze the contents that are left by SNS users, where the individual acts as a sensor. Such analyses have already been attempted by many researchers. In particular, Twitter is used in related spheres in various ways, because it has structural characteristics suitable for detecting events. However, there is a limitation concerning the detection of events and their locations. Thus, we developed a system that can detect the location immediately based on the district mentioned in Twitter. We tested whether the system can function in real time and evaluated its ability to detect events that occurred in reality. We also tried to improve its detection efficiency by removing noise.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.285-302
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• 2013

This study identified topic shifts and patterns over time by analyzing an enormous amount of Twitter data whose characteristics are high accessibility and briefness. First, we extracted keywords for a certain product and used them for representing the topic network allows for intuitive understanding of keywords associated with topics by nodes and edges by co-word analysis. We conducted temporal analysis of term co-occurrence as well as topic modeling to examine the results of network analysis. In addition, the results of comparing topic shifts on Twitter with the corresponding retrieval results from newspapers confirm that Twitter makes immediate responses to news media and spreads the negative issues out quickly. Our findings may suggest that companies utilize the proposed technique to identify public's negative opinions as quickly as possible and to apply for the timely decision making and effective responses to their customers.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.72-82
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• 2013

The present study explored types of Twitter-mediated communication and emotional responses of Twitter users toward celebrities. Three perspectives of para-social interactions, information hub, and fandom were proposed as communication types on Twitter. Celebrities were classified by entertainer, politician, specialist, and blogger. Communication patterns according to each category of celebrities were analyzed. The patterns of emotional responses, which represents the use of emoticons and emotional expressions were also analyzed. The results show that the type of para-social interactions was frequently accepted for the interactions with politicians and specialists, while fandom style was salient for the entertainers. For the power bloggers, the users tend to adopt the type of information hub interaction. The use of emotions and emotional expressions were most frequent in case of fandom style communication and the messages to the entertainers. Implications were further discussed.

• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.571-578
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• 2011

In this study, we suggested a strategy about a discussion class using Twitter, and experimented it inside an elementary school classroom. Elementary students participated in a panel discussion and the others discussed as audience using Twitter. After the discussion, we investigated the effectiveness of our strategy using the Technology Acceptance Model and verified students' satisfaction and ability to collaborate through giving them a questionnaire. As a result, the perceived ease of use positively effected the perceived usefulness and the perceived usefulness influenced the attitude and the attitude affect on intention to use. Also, students were satisfied with the discussion class on Twitter and had a positive perception about collaboration with it. As a result of regression, perception of collaboration among the students influenced the perceived usefulness positively. The results in this study show the effectiveness of using the discussion class strategy on Twitter.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.471-478
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• 2012

Various studies on sentiment classification of documents have been performed. Recently, they have been applied to twitter sentiment classification. However, they did not show good performances because they did not consider the characteristics of tweets such as tweet structure, emoticons, spelling errors, and newly-coined words. In this paper, we perform experiments on various input features (emoticon polarity, retweet polarity, author polarity, and replacement words) which affect twitter sentiment classification model based on machine-learning techniques. In the experiments with a sentiment classification model based on a support vector machine, we found that the emoticon polarity features and the author polarity features can contribute to improve the performance of a twitter sentiment classification model. Then, we found that the retweet polarity features and the replacement words features do not affect the performance of a twitter sentiment classification model contrary to our expectations.