• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• The Journal of Korean Medical History
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• v.24 no.1
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• pp.43-56
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• 2011

Everyone once in a life experience headaches as symptoms are very common. According to a study in a country of more than a week and as many as those who have experienced a headache amounts to 69.4%. In addition, the high reported prevalence of migraine in 30s for 80% of all migraine sufferers daily life interfere with work or was affected. In Western medicine, the cause of headaches is traction or deformation of pain induced tissue like scalp, subcutaneous tissue, muscle, fascia, extracranial arteriovenous, nerves, periosteum. But it turns out there are not cause why pain induced tissue is being tracted or deformated. Therefore, most of the western-therapy is mainly conducted with regimen for a temporary symptom reduction. Therefore, I examined how it has been developed in Chinese Ming Dynasty, the perception of headache, change in disease stage and an etiological cause. Oriental medicine in the treatment of headache is a more fundamental way to have an excellent treatment. The recognition of head in "素問($s{\grave{u}}$ $w{\grave{e}}n$)" and "靈樞($l{\acute{i}}ng$ $sh{\bar{u}}$)" began to appear in 'Soul-神($sh{\acute{e}}n$) dwelling place' and 'where to gather all the Yang-'諸陽之會($zh{\bar{u}}$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $hu{\grave{i}}$)'. Also, head was recognized as '六腑($li{\grave{u}}f{\check{u}}$) 淸陽之氣($q{\bar{i}}ng$ $y{\acute{a}}ng$ $zh{\bar{i}}$ $q{\grave{i}}$) and 五臟($w{\check{u}}$ $z{\grave{a}}ng$) 精血($j{\bar{i}}ng$ $xu{\grave{e}}$) gathering place'. More specific structures such as the brain is considered a sea of marrow(髓海-$su{\check{i}}$ $h{\check{a}}i$) in "內經($n{\grave{e}}i$ $j{\bar{i}}ng$)" and came to recognized place where a stroke occurs. Accompanying development of the recognition about head, there had been changed about the perception of headache and the recognition of the cause and mechanism of headache. And the recognition of headache began to be completed in Ming Dynasty through Jin, Yuan Dynasty. Chinese Ming Dynasty, specially 樓英($l{\acute{o}}u$ $y{\bar{i}}ng$), in "醫學綱目($y{\bar{i}}xu{\acute{e}}$ $g{\bar{a}}ngm{\grave{u}}$)", first enumerated prescription in detail by separating postpartum headache. and proposed treatment of headache especially due to postpartum sepsis(敗血-$b{\grave{a}}i$ $xu{\grave{e}}$). 許浚($x{\check{u}}$ $j{\grave{u}}n$) accepted a variety of views without impartial opinion in explaining one kind of headache in "東醫寶鑑($d{\bar{o}}ng-y{\bar{i}}$ $b{\check{a}}oji{\grave{a}}n)$" 張景岳($zh{\bar{a}}ng$ $j{\check{i}}ng$ $yu{\grave{e}}$), in "景岳全書($j{\check{i}}ng$ $yu{\grave{e}}$ $qu{\acute{a}}nsh{\bar{u}}$)", established his own unique classification system-新舊表裏($x{\bar{i}}nji{\grave{u}}$ $bi{\check{a}}ol{\check{i}}$)-, and offered a clear way even in treatment. Acupuncture treatment of headache in the choice of meridian has been developed as a single acupuncture point. Using the classification of headache to come for future generation as a way of locating acupoints were developed. Chinese Ming Dynasty, there are special treatments like 導引按蹻法($d{\check{a}}o$ y ${\check{i}}n$ ${\grave{a}}n$ $ji{\check{a}}o$ $f{\check{a}}$), 搐鼻法($ch{\grave{u}}$ $b{\acute{i}}$ $f{\check{a}})$, 吐法($t{\check{u}}$ $f{\check{a}}$), 外貼法($w{\grave{a}}i$ $ti{\bar{e}}$ $f{\check{a}}$), 熨法($y{\grave{u}}n$ $f{\check{a}}$), 點眼法($di{\check{a}}n$ $y{\check{a}}n$ $f{\check{a}}$), 熏蒸法($x{\bar{u}}nzh{\bar{e}}ng$ $f{\check{a}}$), 香氣療法($xi{\bar{a}}ngq{\grave{i}}$ $li{\acute{a}}of{\check{a}}$). Most of this therapy in the treatment of headache, it is not used here, but if you use a good fit for today's environment can make a difference.

• Journal of the Korean Society of Food Culture
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• v.18 no.4
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• pp.333-345
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• 2003

We conducted a survey on Japanese women's consciousness of food culture and their traditional food consumption by self filling-out questionnaire during January, 2000 for the period of a month, For the survey we selected 250 women residing in Kyoto, Japan. For the statistic work we used SAS package system, and t-test, $\cal{X}^2-test$ and Duncan's multiple range test were also used to verify the results significance. The purpose of this survey lies in gathering a basic data on the comparative direction of Korean and Japanese women's food culture in the future 1. Comparing the preferred food purchase place, In case of Korean women, traditional market was comparatively more preferred while Japanese women relatively preferred convenience store (p<0.001). 2. In case of Japanese women, they answered there is no difference from ordinary days on New Year's Day (71%) and Christmas (40%) while 38% answered they prepare food at home. 40% said they prepare food on parents-in-law's birthday, and 41% said no difference from ordinary days. 52% said they prepare food at home on husband's birthday. For their own birthday, 32% said yes to preparing food at home while 45% said no difference and 22.3% said eating out. For children's birthday 65% said preparing at home, 16.3% said no difference and 14.9% said eating out. 3. Comparing the conception on traditional food, Korean women answered 'complicated' (77%) most while 'simple' (5%) least, which indicates their demands for simplified recipes. In case of Japanese women, 'complicated' (44%) was most while 'scientific' (6%) was least which indicates their demands for scientific way of recipes. There were differences shown by age (p<0.001) and the older the more said 'simple' or 'logical' (p<0.01). 4. As the reason for the complicity of traditional food recipes, Koreans said 'too many hand skill' (60%) most while 'too many spices' (8%) least. For Japanese, 'various kind of the recipe' (55%) was most while 'too many hand skill' (7%) was least. There were significant differences shown by academic background (p<0.01) and income(p<0.01), and the lower the academic background, the more said 'too many spices' as the reason for the complicity in making traditional food. Generally, the lesser the income, the more tendency to say 'various kinds of the recipe'. 5. In case of Koreans, 'the recipe is difficult' (56%) was high while 'uninterested' (9%) was low in answer which showed differences by academic background (p<0.05), and in case of Japanese, 'no time to cook' (44%) was high while 'uninterested' (7%) was low. 6. The following is the reasons for choosing traditional food as a snack for children. In case of Koreans, they answered as 'traditional food' (34%), 'made from nutrious and quality materials' (27%), 'for education' (22%) and 'suites their taste' (17%) revealing 'traditional food' is highest. In case of Japanese, it was revealed in the order of 'made from nutrious and quality materials' (36.3%), 'traditional food' (25.2%), 'suites their taste' (22.6%), 'for education' (12.8%) and 7. Comparing the most important thing for the popularization of traditional food in the world, Koreans answered 'taste and nutrition' (45%) most while 'shape and color' (6%) least. In case of Japanese, 'taste and nutrition' (75%) was answered most while 'hygienic packaging' (4%) was least. Both considered 'taste and nutrition' as most important thing for the popularization of traditional food in the world. 8. In case of Koreans, they answered they learn how to make traditional food 'from mother' (47%), 'media' (18%), 'school' (15%), 'from mother-in-law' (14%), 'private cooking school' (4%) and 'close acquaintances' (2%). In case of Japanese, they said mostly learn 'from mother', but it was also shown that the lower the academic background the lesser the tendency of learning 'from mother' but 'from school' (p<0.001). 9. About the consumption of traditional fermented food, Koreans said they make kimchi (90%), pickled vegetables (39%), soy sauce (33%), bean paste (38%), salted fishery (12%) and traditional liquors (14%) at home while 67% for salted fishery and 48% for traditional liquors answered they buy rather than making at home. On the other hand, Japanese answered they mostly buy kimchi (60%), soy sauce (96%), bean paste(91%), natto(92%), salt fermented fish foods (77%) and traditional alcoholic beverage (88%) to eat. This difference was shown very distinct between Korean and Japanese women (p<0.001). 10. About the most important thing in food, Koreans answered in the order of 'liking and satisfaction' (33%), 'for health' (32%), 'for relieve hunger' (18%) and 'convenience' (17%). In case of Japanese, it was revealed in the order of 'for health' (61%), 'liking and satisfaction' (20%), 'to relieve hunger' (16%) and 'convenience' (3%). This shows that Japanese women take comparably more importance to health than Korean women. The conception of food was shown different between Korean and Japanese women (p<0.001), and Koreans showed level 4-5 of food culture while Japanese showed level 5.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Korean Journal of Heritage: History & Science
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• v.36
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• pp.347-377
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• 2003

China is a country with large agricultural areas and subject to frequent calamities. Drought is the top of them. It has been a key problem for development of agriculture in the country. In the long struggle against drought, Chinese have accumulated many rational and irrational experiences. The Dragon Kings Belief, which is popular in North China and discussed in a thesis, is one of their irrational experiences. The belief was passed together with Buddhism from India to China in the Tang Dynasty. After it settled down, it was incorporated with the local five dragons belief and a set of beliefs in dragon kings came into existence. The emergence of the dragon kings belief ended the history that the title of rain got was not clear in China and Dragon kings finally got the status. Irrigation is the lifeblood of agriculture in China. In a Chinese mind, Dragon kings are the most important gods who take charge of rain and thus offer the lifeblood. In understanding the nature and characteristics of Chinese traditional culture, it is important for us to make clear the origin and evolution of the belief, find out its nature, function and operation. In the every year beginning of February of the Fanzhuang calendar in the people of Hebeisheng Zhaoxian, would all hold a festival to offer sacrifices to the $^{{\circ}TM}^{\prime}longpai$. Longpai was regarded as the core of the temple fair, thus the native sons came to call this festival; "longpaihui". In this region the'Fanzhuang longpaihui'developed into a well knownand grand temple fair. It was able to attract numerous pilgrims with its special magic power, occupying a place in $China^{{\circ}TM}$ 'eryueer'festival with festive dragon activities. The dragon is a common totem among Chinese nationals. The belief worship of the dragon dates from the start time of primitive societies. Dragon oneself the ancients worship's thunder lightning. In the worship of the great universe, at first afterwards this belief with the tribe's totem worships to combine to become the animal spirit. In ancient myths legends, along with folk religion and beliefs all hold a very important position. The longpaihui is a temple fair without a temple; this characteristic is a distinction between longpaihui and other temple fairs. As for longpaihui must of the early historical records are unclear. The originator of a huitou system has a kind of organized form of the special features rather, originator of a huitou not fix constant, everything follows voluntarily principle, can become member with the freedom, also can back at any time the meeting. There is a longpaihui for 'dangjiaren', is total representative director in the originator of a huitou will. 'banghui' scope particularly for extensive, come apparently every kind of buildup that help can return into the banghui, where is the person of this village or outside village of, the general cent in banghui work is clear and definite, for longpaihui would various businesses open smoothly the exhibition provides to guarantees powerfully. Fanzhuang longpaihui from the beginning of February to beginning six proceed six days totally. The longpai is used as the ancestry absolute being to exsits with the community absolute being at the same time in fanzhuang first took civil faith, in reality is a kind of method to support social machine in native folks realize together that local community that important function, it provided a space, a kind of a view to take with a relation, rising contact, communication, solidify the community contents small village, formation with fanzhuang. The fanzhuang is used as supplies for gathering town, by luck too is this local community trade exchanges center at the same time therefore can say the faith of the longpai, in addition to its people's custom, religious meaning, still have got the important and social function. Moreover matter worthy of mentioning, Longpai would in organize process, from prepare and plan the producing of meeting every kind of meeting a longpeng of the matter do, all letting person feeling is to adjust the popular support of, get the mass approbation with positive participate. Apart from the originator of a huitou excluding, those although not originator of a huitou, however enthusiasm participate the banghui of its business, also is too much for the number.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.6
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• pp.25-38
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• 2020

The objective of this study is to propose a direction for creating a memorial park for the 250 students victims of the Sewol ferry disaster. To this end, this study first attempted to understand the matters discussed at various levels to create a memorial park and find a way that the park can be built by gathering opinions from the bereaved families and the victims themselves, as well as local residents, and experts. Workshops, competitions, special lectures, and websites, etc, were analyzed. A social discourse analysis methodology was used for systematic analysis, and the analyzed discourse was categorized into 4 types for assessment, and the functions and roles were subdivided into 15 types. To assess the priorities and the adequacy of the discourse, an analytic hierarchy process (AHP) was used among 30 activists, public servants, and experts. Then, a survey was conducted to analyze the perception of the residents (467 participants including the bereaved families) about the memorial park. Based on the results of the analysis, two directions were set for the memorial park. First, is a memorial park to remember the victims in everyday life. It must be a park with various cultural contents instead of a conventional memorial park that is solemn and grave sharing anguish and sorrow. The memorial park for the Sewol ferry disaster must become a space where visitors can naturally encounter and remember the victims. Second, is a park that serves as a catalyst that brings change and innovation to the community. It must be able to bring change to the community with direct and indirect influence. It must serve as an impetus to bring change and innovation to the community in the mid-to-long-term. Having many visitors may also lead to an economic effect. These visitors may not just stay in the park, but even contribute to revitalizing the local businesses. The purpose of this study is to apply the research findings to guide the International Design Competition scheduled for 2020 and serve to establish guidelines for a continuous park management system.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.3
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• pp.220-237
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• 2021

Since the functional importance of marine phytoplankton was firstly advocated from early 1880s massive data on the species composition and abundance were produced by classical microscopic observation and the advanced auto-imaging technologies. Recently, pigment composition resulted from direct chemical analysis of phytoplankton samples or indirect remote sensing could be used for the group-specific quantification, which leads us to more diversified data production methods and for more improved spatiotemporal accessibilities to the target data-gathering points. In quite a few cases of many long-term marine ecosystem monitoring programs the phytoplankton species composition and abundance was included as a basic monitoring item. The phytoplankton data could be utilized as a crucial evidence for the long-term change in phytoplankton community structure and ecological functioning at the monitoring stations. Usability of the phytoplankton data sometimes is restricted by the differences in data producers throughout the whole monitoring period. Methods for sample treatments, analyses, and species identification of the phytoplankton species could be inconsistent among the different data producers and the monitoring years. In-depth study to determine the precise quantitative values of the phytoplankton species composition and abundance might be begun by Victor Hensen in late 1880s. International discussion on the quality assurance of the marine phytoplankton data began in 1969 by the SCOR Working Group 33 of ICSU. Final report of the Working group in 1974 (UNESCO Technical Papers in Marine Science 18) was later revised and published as the UNESCO Monographs on oceanographic methodology 6. The BEQUALM project, the former body of IPI (International Phytoplankton Intercomparison) for marine phytoplankton data QA/QC under ISO standard, was initiated in late 1990. The IPI is promoting international collaboration for all the participating countries to apply the QA/QC standard established from the 20 years long experience and practices. In Korea, however, such a QA/QC standard for marine phytoplankton species composition and abundance data is not well established by law, whereas that for marine chemical data from measurements and analysis has been already set up and managed. The first priority might be to establish a QA/QC standard system for species composition and abundance data of marine phytoplankton, then to be extended to other functional groups at the higher consumer level of marine food webs.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• 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.