• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.217-222
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• 2011

SNS is growing by leaps and bounds, and many users of SNS are using by a medium of communication. Using SNS users are using means of their own news and the change of emotional expression. In this study using emotional elements to the program was implemented to classify the message. Extraction of emotional elements were used for emotional vocabulary in OMLS (Ocean-Monmouth Legal Services). Emotional elements were extended by The Roget of the thesaurus and WordNet.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.352-359
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• 2022

The study examined a sentiment analysis based on Tweeter messages between contemporary pop musicians and classical music composers. Musicians of each genre were carefully selected for the sentiment analysis. Many opinion messages on Tweets that users have discussed were collected, and the messages were evaluated by using Naïve Bayes Classifier. The results demonstrated that users showed high positive sentiments for the two different genres. However, on average, the positive sentiment values for classical music composers are higher than for contemporary pop musicians. In addition, the rankings of the highest positive sentiments among contemporary pop musicians and classical music composers did not coincide with the popularity of the two different genres of musicians. This study will contribute to the study of future sentimental analysis between music and musicians.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.35-43
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• 2023

This paper emphasizes that information received in disaster situations can lead to disparities in the effectiveness of communication, potentially causing damage. As a result, there is a growing demand for disaster and safety information among citizens. A user-centered disaster and safety information application service is designed to address the rapid dissemination of disaster and safety-related information, bridge information gaps, and alleviate anxiety. Through the Open API (Open Application Programming Interface), we can obtain clear information about the weather, air quality, and guidelines for disaster-related actions. Using chatbots, we can provide users with information and support decision-making based on their queries and choices, utilizing cloud APIs, public data portal open APIs, and solution knowledge bases. Additionally, through Mashup techniques with the Google Maps API and Twitter API, we can extract various disaster-related information, such as the time and location of disaster occurrences, update this information in the disaster database, and share it with users.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.112-119
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• 2023

Traditionally used for networking computers and communications, the Internet has been evolving from the beginning. Internet is the backbone for many things on the web including social media. The concept of social networking which started in the early 1990s has also been growing with the internet. Social Networking Sites (SNSs) sprung and stayed back to an important element of internet usage mainly due to the services or provisions they allow on the web. Twitter and Facebook have become the primary means by which most individuals keep in touch with others and carry on substantive conversations. These sites allow the posting of photos, videos and support audio and video storage on the sites which can be shared amongst users. Although an attractive option, these provisions have also culminated in issues for these sites like posting offensive material. Though not always, users of SNSs have their share in promoting hate by their words or speeches which is difficult to be curtailed after being uploaded in the media. Hence, this article outlines a process for extracting user reviews from the Twitter corpus in order to identify instances of hate speech. Through the use of MPCA (Modified Principal Component Analysis) and ECNN, we are able to identify instances of hate speech in the text (Enhanced Convolutional Neural Network). With the use of NLP, a fully autonomous system for assessing syntax and meaning can be established (NLP). There is a strong emphasis on pre-processing, feature extraction, and classification. Cleansing the text by removing extra spaces, punctuation, and stop words is what normalization is all about. In the process of extracting features, these features that have already been processed are used. During the feature extraction process, the MPCA algorithm is used. It takes a set of related features and pulls out the ones that tell us the most about the dataset we give itThe proposed categorization method is then put forth as a means of detecting instances of hate speech or abusive language. It is argued that ECNN is superior to other methods for identifying hateful content online. It can take in massive amounts of data and quickly return accurate results, especially for larger datasets. As a result, the proposed MPCA+ECNN algorithm improves not only the F-measure values, but also the accuracy, precision, and recall.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.319-340
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• 2013

The purposes of this study are to understand the current status of using and adoption of social network services for public libraries in Korea and to propose how to activate social network services for the libraries in the future. For this study, the usage of the social network services by 166 public libraries was investigated. In addition, surveys to 40 library representatives from the libraries were conducted and 198 public library users answered users' survey questionnaires. As methods to activate social network services in pubic libraries, providing education and training for librarians, hiring librarians for the service, and monitoring and educating the library users for participating with diversifying promotion channels for the service were suggested.

• Knowledge Management Research
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• v.13 no.3
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• pp.1-16
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• 2012

Nowadays, many first-time Internet users start off heavily using SNSs (Social Network Sites), such as Cyworld, Facebook, and Twitter. The reason for the growth of SNS use is closely related to the various services of gaming, playing, using entertainment items, sharing knowledge etc., provided by the SNS; technically, the most important of the services provided would be the behavior of sharing knowledge among people connected and networked in the site. In sum, we assume that the users may communicate well with each other and pay attention to building a close social network using the abovementioned activities. However, researchers have just begun to focus on the issues explaining why Internet users rush into SNSs and enjoy their time there. Therefore, we investigated the reasons for posting and sharing knowledge voluntarily on the SNS and how others respond to the posted knowledge and are actually affected by the behavior. We applied social identity theory and social capital theory in this study to find which network externalities in SNSs may affect online identity-based attachment and cause them to produce a knowledge sharing generation. We found that people's online identity in SNSs is closely related to and influences knowledge sharing. This empirical study resulted in the importance of social relations in SNSs, which leads to sharing knowledge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2871-2877
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• 2015

Shopping patterns are changing with the emergence of SNS. Recently, it is also interested in providing the information based on the users' needs. Generally, the provided information is obtained from the history of users' simple browsing. Best selling hot item list is also provided in order to reflect the preferences of public users. However, the provided information is irrelevant to an individual preference. In this paper, we propose a method to utilize inner and outer SNS for analyzing public preferences about goods which are interested by individual users. The inner analyzing module collects and analyzes the preferences of community members about two goods designated by individual users. The outer analyzing module supports to analyze public preferences by using the tweeter SNS. The results of implementation show that it is possible to recommend goods based on the individual users' preferences unlike the existing shopping mall.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.407-419
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• 2015

As the use of social media has gained mainstream popularity, concerns on the particulars of the users' motivation and method of use is on the rise. Under uses and gratifications approach, this study analyzes the use behaviors in Kakao-story, Facebook, and Twitter, and analyzes the changes in the perception of social support by the users of the media. Data was analyzed from 240 online questionnaire surveys. The study finds that the frequency of use of social media differs depending upon the personality of the users and their purpose of use, both of which also influence their type of use. It also finds that frequent use of social media increased the users' perception of social support, whereas their type of use had no effect with such perception. Communication behavior through social media seem to increase perception of social support.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.105-117
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• 2014

The change of social media market toward multimedia environment makes users select social media according to preference factor's gratification and this also causes competition among various social medias. So this study focused on competition among social media from the perspective of users' gratification while considering multimedia environment of social media market. The widely known Niche theory is used to confirm competitions among media in an environment with limited resource. According to research result, (1) Facebook and Kakao Talk mostly satisfies users' expectations; (2) Facebook and Kakao Talk form leading group and Blog, Youtube and Twitter form chasing group in this competition; (3) Kakao Talk greatly satisfies users' various expectations. The research result implies that, for social media to have competitive advantage in the market, it is better to provide convenience and real-time responsiveness in mobile environment and to improve service so that users could more easily utilize network.