• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.1-11
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• 2021

The users of a chatbot messenger can be better engaged in the conversation if they feel intimacy with the chatbot. This can be achieved by the chatbot's effective expressions of human emotions to chatbot users. Thus motivated, this study aims to identify the appropriate emotional expressions of a chatbot that make people feel the social presence of the chatbot. In the background research, we obtained that facial expression is the most effective way of emotions and movement is important for relationship emersion. In a survey, we prepared moving text, moving gestures, and still emoticon that represent five emotions such as happiness, sadness, surprise, fear, and anger. Then, we asked the best way for them to feel social presence with a chatbot in each emotion. We found that, for an arousal and pleasant emotion such as 'happiness', people prefer moving gesture and text most while for unpleasant emotions such as 'sadness' and 'anger', people prefer emoticons. Lastly, for the neutral emotions such as 'surprise' and 'fear', people tend to select moving text that delivers clear meaning. We expect that this results of the study are useful for developing emotional chatbots that enable more effective conversations with users.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.11-17
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• 2009

It's a trend that the proportion of using an internet messenger among on-line communication methods is getting increased. However, there are not many applications which efficiently utilize these messenger communication data. Messenger communication data have specific characteristics that reflect the user's linguistic habits. The linguistic habits are revealed through frequently used words and emoticons, and user's emotions can be grasped by these. This paper proposes the method that efficiently classifies the emotions of a messenger user using frequently used words or symbols. The emotion classifier from repeated experiments achieves high accuracy of more than 95%.

• Cartoon and Animation Studies
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• s.51
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• pp.361-390
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• 2018

Users of KakaoTalk emoticons use Kakao Talk emoticons as means of communicating their emotions in virtual space. Emotional state is represented by design element (auxiliary, color, form, motion) and storytelling element contained in emoticons. The purpose of this study is to investigate the factors of the storytelling and design elements of kakaoTalk emoticons and how they prefer the kakaoTalk emoticons as emotional expression means. In terms of storytelling, crocodiles, peaches, dogs, ducks, lions, moles, and rabbits were made up of ordinary fruits and animals. Most of the emoticons are composed of stories with unique personality, and each story has a complex one by one, which makes it easy for users to approach and use them. In terms of design, I used various auxiliary elements (flame, sweat, tears, runny nose, angry eyes, etc.) to express angry, sincere, nervous, begging, joy, and sadness. The color elements consisted of most of the warm color series with the unique colors (green, red, yellow, pink, white, black, brown, etc.) of emoticon characters regardless of feelings of joy, anger, sadness, pleasure. The form factor is composed of a round shape when expressing factors such as joy and sadness. On the other hand, when FRODO and NEO express sadness and anger, they represent the shape of a rectangle. The motion elements are horizontal, vertical, and oblique expressions of APPEACH, NEO, TUBE, and JAY-G, expressing emotional expressions of sadness, anger, and pleasure. APEACH, TUBE, MUZI & / Shows the dynamic impression of the oblique and the radiation / back / forward / rotation. The anger of TUBE and FRODO shows horizontal / vertical / diagonal and radial motion. As a result of this study, storytelling is structured in accordance with each emoticon character. In terms of design, auxiliary elements such as flame, sweat, and tears are represented by images. The color elements used the unique colors of the character series regardless of the difference of emotion. The form factor represented various movements for each emotion expression. These findings will contribute to the development of communication, emotional design and industrial aspects. Despite the significance of the above paper, I would like to point out that the analysis framework of the storytelling and the semiotic analysis of the supplementary elements are not considered as limitations of the study.

• Archives of design research
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• v.20 no.1 s.69
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• pp.133-142
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• 2007

Unlike face-to-face communication, text-based communication by digital media has limitations that non-verbal elements are eliminated and social presence decrease. To overcome this problem, people try to find solutions which visualize emotion and situation by using emoticons, icons, computer language and so on. As most SMS users experience the failure of using emotions on the mobile phone, they need to make up for this point. In this study, we conducted research on the recent mobile fonts situations and surveyed users' perception on SMS fonts as to suggest solutions of expressing and visualizing emotions on the mobile phone, a representative media of personal communication. As a solution of reducing the failure, we conducted a survey on users' perception about fonts and the capability of the expressing emotions by fonts. The survey found that mobile fonts can be used as a method to express human emotion. As a finding, the shape of the font can be used as a method to visualize the emotion through text messaging. In future studies, such a method can be applied to variety of different personal media with the communication method based on text. Those studies can propose different usage for fonts in communication.

• Journal of Korea Multimedia Society
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• v.10 no.4
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• pp.506-515
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• 2007

In this paper, we propose a Real-time Interactive Shadow Avatar(RISA) which can express facial emotions changing as response of user's gestures. The avatar's shape is a virtual Shadow constructed from the real-time sampled picture of user's shape. Several predefined facial animations overlap on the face area of the virtual Shadow, according to the types of hand gestures. We use the background subtraction method to separate the virtual Shadow, and a simplified region-based tracking method is adopted for tracking hand positions and detecting hand gestures. In order to express smooth change of emotions, we use a refined morphing method which uses many more frames in contrast with traditional dynamic emoticons. RISA can be directly applied to the area of interface media arts and we expect the detecting scheme of RISA would be utilized as an alternative media interface for DMB and camera phones which need simple input devices, in the near future.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.411-416
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• 2008

The development of games has brought about the birth of game characters that are visually very realistic. At present, one sees much enthusiasm for giving the characters emotions through such devices as avatars and emoticons. However, in a freely changing environment of games, the devices merely allow for the expression of the value derived from a first input rather than creating expressions of emotion that actively respond to their surroundings. As such, there are as of yet no displays of deep emotions among game characters. In light of this, the present article proposes the 'CROSS(Character Reaction on Specific Situation) Model AE Engine' for game characters in order to develop characters that will actively express action and emotion within the environment of the changing face of games. This is accomplished by classifying the emotional components applicable to game characters based on the OCC model, which is one of the most well known cognitive psychological models. Then, the situation of game playing analysis of the commercialized RPG game is systematized by ontology.

• 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.

• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.3-16
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• 2021

Studies have found that people with higher social status pay little attention to other people's emotions and facial expressions. However, only a few studies have made similar observations on adolescents with high cyberspace social status. Therefore, this study sought to identify how adolescents with different online game character social statuses interpreted the smile emoticons in negative and positive situations, that is, did they perceive the emoticon to be positive (smile, encouragement, and consolation) or negative (derision, ridicule, and sarcasm). In Experiment 1, the participants were separated into three groups; those who had a lower than global average online game character status, those who had the same as the global average, and those who had higher than the global average. The participants were then asked to judge the meaning of the smile emoticon received in various positive or negative situations. In Experiment 2, the game character levels of the participants were set to be either higher or lower than the others' characters, and they were again asked to judge the meaning of the smile emoticon received in the positive or negative situations. In Experiment 3, the participants were separated into four groups; lower level than the average game character status (no information on the level of acquaintance's game character), lower than the average but higher than the character of the other, higher than the average status (no information on the other's character level), and higher than the average but lower than the character of the other, and asked to judge the meaning of the smile emoticon in positive or negative situations. It was found that when participants had a lower-level character compared to the average, had a lower-level character than the other, and had higher than the average but lower than the other's character, they interpreted the smile emoticon as derision, ridicule, or sarcasm. However, participants with higher level characters, higher than that of the other, and lower than the average but higher than the other interpreted the emoticon as a smile or consolation. This study was significant because it demonstrated the impact of an adolescent's social cyberspace status on their online communication.

• 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.