• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.118-127
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• 2020

The paper introduces trends and methodological challenges of quantitative Korean text analysis by using the case studies of academic and news media articles on "migration" and "immigration" within the periods of 2017-2019. The quantitative text analysis based on natural language processing technology (NLP) and this became an essential tool for social science. It is a part of data science that converts documents into structured data and performs hypothesis discovery and verification as the data and visualize data. Furthermore, we examed the commonly applied social scientific statistical models of quantitative text analysis by using Natural Language Processing (NLP) with R programming and Quanteda.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.101-105
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• 1996

근래에 기존의 텔레비젼이나 라디오 방송에서 영상이나 음성의 방송 외에, 할당된 주파수를 최대한 효율적으로 사용하기 위한 부가방송의 개발이 활발히 이루어지고 있다. KBS에서는 오래 전부터 데이터 방송분야의 개발에 힘써왔으며 라디오에서는 RDS를 이용한 데이터 방송이 실용화 단계에 들어가고 있다. 1995년 하반기부터 KBS 제1라디오(표준FM)를 통하여 전국적으로 RDS 시험 방송을 하고 있으며, 또한 1997년에 문자정보 서비스를 위해 한국 실정에 맞는 새로운 RDS 한글 문자정보 규격을 만들어 서비스를 할 예정이다.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further 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 Digital Convergence
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• v.18 no.2
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• pp.249-259
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• 2020

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set's dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.97-104
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• 2012

Recently, a big text data has been produced by users, an opinion mining to analyze information and opinion about users is becoming a hot issue. Of the opinion mining, especially a sentiment analysis is a study for analysing emotions such as a positive, negative, happiness, sadness, and so on analysing personal opinions or emotions for commercial products, social issues and opinions of politician. To analyze the sentiment analysis, previous studies used a mapping method setting up a distribution of emotions using two dimensions composed of a valence and arousal. But previous studies set up a distribution of emotions arbitrarily. In order to solve the problem, we composed a distribution of 12 emotions through carrying out a survey using Korean emotion words list. Also, certain emotional states on two dimension overlapping multiple emotions, we proposed a selection method with Roulette wheel method using a selection probability. The proposed method shows to classify a text into emotion extracting emotion terms from a text.

• Journal of Broadcast Engineering
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• v.18 no.4
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• pp.559-571
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• 2013

This paper has presented a few problems when the 2D video superimposed the overlay text was converted to the 3D stereoscopic video. To resolve the problems, it proposes the scenario which the original video is divided into two parts, one is the video only with overlay text graphic region and the other is the video with holes, and then processed respectively. And this paper focuses on research only to detect and extract the overlay text graphic region, which is a first step among the processes in the proposed scenario. To decide whether the overlay text is included or not within a frame, it is used the corner density map based on the Harris corner detector. Following that, the overlay text region is extracted using the hybrid method of color and motion information of the overlay text region. The experiment shows the results of the overlay text region detection and extraction process in a few genre video sequence.

• Archives of design research
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• v.16 no.4
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• pp.375-384
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• 2003

Interactive storytelling is a scenario created "on the fly" with digital content through user interaction. Every time interaction occurs between the user and content, a brand new story is created. Interaction intrigues people because it provides different story from same content. Through conventional media, people shared same content and experience. However through interactive media, people encounter unique experience, over same content possibly everytime they use it. People we, by their nature, very interactive being. However, interacting with media is not an activity that people are accustomed to. Hence, designing content has been all migrating experience from existing media to an unfamiliar ground. Unique and adoptive ways of designing content for digital interactive media is being sought out from the need as the result of the evolution of integrated society and emerging information technology. People are already used to some of interactive storytelling through hyper text in CD-ROM and web sites. More complicated and different structured models were born through games that offered graphics, virtual spaces and interactivity. When drawn onto a structural graph, few attributes and similarities seem to occur. This paper will try to outline and discuss structural graphs of interactive storytelling methods and suggest some ways for better storytelling design.

• Journal of Fashion Business
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• v.22 no.4
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• pp.20-29
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• 2018

Today, visitors of art galleries like to share their life in their communities than interacting with artwork. Meantime, image sharing of an exhibition on social media has become more important than actual watching of the artwork. Accordingly, most of the galleries have started paying more attention in organizing an exhibition environment for proof-shots to attract more visitors. We initially conducted research about the internet environment from the late 1990s to the recent years and analyzed the changing watching patterns of the exhibition since the advent of social media. Secondly, for empirical case analysis, we selected 'Plastic Fantastic' held in D-Museum as the target of analysis. The analysis targeted 500 recent postings that were discovered on Instagram on March 4, 2018, as 'Plastic-Fantastic'(in Korean). The methods of analysis included classification types of image, hashtag, and text on Instagram and were arranged in an order of relation to the exhibits. Based on the image analysis, 44.2% of the images involved exhibition displays; the others included a person or other goods. Based on the results of the text and hashtag analysis, only 3.6% of posting included information about the exhibition and 56.4% had non-related inflow hashtags only with image. The behavior of these shares is likely to gradually lose the inherent meaning of the exhibition and to the value rather than imparting the artistic thrill that viewers derive from art. Exhibition should try to seek deep interaction between the display, audience, and social media users, rather than encouraging the visitors to take proof-shots.