• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.63-82
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• 2016

Individuals gather information online to resolve problems in their daily lives and make various decisions about the purchase of products or services. With the revolutionary development of information technology, Web 2.0 has allowed more people to easily generate and use online reviews such that the volume of information is rapidly increasing, and the usefulness and significance of analyzing the unstructured data have also increased. This paper presents an analysis on the lexical features of expert product reviews to determine their influence on consumers' purchasing decisions. The focus was on how unstructured data can be organized and used in diverse contexts through text mining. In addition, diverse lexical features of expert reviews of contents provided by a third-party review site were extracted and defined. Expert reviews are defined as evaluations by people who have expert knowledge about specific products or services in newspapers or magazines; this type of review is also called a critic review. Consumers who purchased products before the widespread use of the Internet were able to access expert reviews through newspapers or magazines; thus, they were not able to access many of them. Recently, however, major media also now provide online services so that people can more easily and affordably access expert reviews compared to the past. The reason why diverse reviews from experts in several fields are important is that there is an information asymmetry where some information is not shared among consumers and sellers. The information asymmetry can be resolved with information provided by third parties with expertise to consumers. Then, consumers can read expert reviews and make purchasing decisions by considering the abundant information on products or services. Therefore, expert reviews play an important role in consumers' purchasing decisions and the performance of companies across diverse industries. If the influence of qualitative data such as reviews or assessment after the purchase of products can be separately identified from the quantitative data resources, such as the actual quality of products or price, it is possible to identify which aspects of product reviews hamper or promote product sales. Previous studies have focused on the characteristics of the experts themselves, such as the expertise and credibility of sources regarding expert reviews; however, these studies did not suggest the influence of the linguistic features of experts' product reviews on consumers' overall evaluation. However, this study focused on experts' recommendations and evaluations to reveal the lexical features of expert reviews and whether such features influence consumers' overall evaluations and purchasing decisions. Real expert product reviews were analyzed based on the suggested methodology, and five lexical features of expert reviews were ultimately determined. Specifically, the "review depth" (i.e., degree of detail of the expert's product analysis), and "lack of assurance" (i.e., degree of confidence that the expert has in the evaluation) have statistically significant effects on consumers' product evaluations. In contrast, the "positive polarity" (i.e., the degree of positivity of an expert's evaluations) has an insignificant effect, while the "negative polarity" (i.e., the degree of negativity of an expert's evaluations) has a significant negative effect on consumers' product evaluations. Finally, the "social orientation" (i.e., the degree of how many social expressions experts include in their reviews) does not have a significant effect on consumers' product evaluations. In summary, the lexical properties of the product reviews were defined according to each relevant factor. Then, the influence of each linguistic factor of expert reviews on the consumers' final evaluations was tested. In addition, a test was performed on whether each linguistic factor influencing consumers' product evaluations differs depending on the lexical features. The results of these analyses should provide guidelines on how individuals process massive volumes of unstructured data depending on lexical features in various contexts and how companies can use this mechanism from their perspective. This paper provides several theoretical and practical contributions, such as the proposal of a new methodology and its application to real data.

• Korean Journal of Food Science and Technology
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• v.37 no.5
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• pp.768-775
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• 2005

Lentinus edodes powder was added at 1-3%(w/w) to improve functional properties of jocheong. Content of crude protein, ash, crude lipids, total mineral, free sugar and reducing sugar increased with increasing amount of L. edodes powder, while viscosity and solid and carbohydrate contents decreased. Through amino acid analysis, 17 amino acids were identified and quantified, glutamic acid being the major amino acid. No significant differences were observed in fatty acid composition and pH between control and L. edodes powder-added jocheong. Addition of mushroom powder in jocheong decreased lightness, yellowness and redness in Hunter's color value. Sensor score of jucheong containing 1% of L. edodes powder was similar to that of control. Results showed jocheong containing less than 2% L. edodes powder gave highest scores in quality characteristics and sensory evaluation.

• Cartoon and Animation Studies
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• s.36
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• pp.469-491
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• 2014

Webtoon has developed the method that makes it possible to express sound visually. Also we can also hear sound in webtoon through the development of web technology. It is natural that we analyze the sound that we can hear, but we can also analyze the sound that we can not hear. This study is based on 'dual code' in cognitive psychology. Cartoonists can make visual expression on the basis of auditive impression and memory, and readers can recall the sound through the process of memory and memory-retrieval. This study analyzes both audible sound and inaudable sound. Concise analysis owes the method to film sound theory. Three main factor, Volume, pitch, and tone are recognized by frequency in acoustics. On the other hand they are expressed by the thickness and site of line and image of sound source. The visual expression of in screen sound and off screen sound is related to the frame of comics. Generally the outside of frame means off sound, but some off sound is in the frame. In addition, horror comics use much sound for the effect of genre like horror film. When analyzing comics sound using this kinds of the method film sound analysis, we can find that webtoon has developed creative expression method comparing with simple ones of early comics. Especially arranging frames and expressing sound following and vertical moving are new ones in webtoon. Also types and arrangement of frame has been varied. BGM is the first in using audible sound and recently BGM composed mixing sound effect is being used. In addition, the program which makes it possible for readers to hear sound according to scroll moving. Especially horror genre raise the genre effects using this technology. Various methods of visualizing sound are being created, and the change shows that webtoon could be the model of convergence in contents.

• Trans-
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• v.12
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• pp.51-79
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• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.

• 대한공업교육학회지
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• v.37 no.1
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• pp.183-204
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• 2012

This study was to analysis perception about Teaching & Learning Support System of the school of Rural Areas in Korea. In order to accomplish the study purpose, the study conducted a research through Web Survey method and examined their recognition. Major findings of this study were as follows: First, Student was satisfied with TLSS of the school of Rural Areas in Korea. Students would like to 'using e-learning with Electronic board and Tablet PC' among program contents. But the system' utilization was low. However, 90% of the students wanted that teacher usually using the FASTEL. Second, Teacher satisfaction was lower compared to the students is relatively. Teachers prefer 'various after school program' to 'using e-learning with Electronic board and Tablet PC'. However, learning effective of using FASTEL system was a 73.8% of teachers are effective is aware and had. Third, 35% Students recognized that are using the pastel system, frequently, On the other hand, the teachers are 60% and to recognize that there was. The reason doesn't the system to good use is slow wireless. Still, e-learning classroom infrastructure for satisfaction was high. Fourth, Teachers recognized students and the pastel system interaction than other e-learning system high. FASTEL System used in the most uncomfortable things on e-learning classroom Wireless Internet phenomena and lost content lacking, in order, I was told. Therefore, there is an urgent need to improve to include Wireless Internet speed improvements and various content added support, easy-to-use instructional tools Add support order selected.