• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Distribution Research
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• v.16 no.1
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• pp.95-115
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• 2011

Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.75-90
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• 2003

When we think of fundamental problems of the aquaculture industry, there are several strict conditions, and consequently the aquaculture industry is forced to change. Fish aquaculture has a structural supply surplus in production, aggravation of fishing grounds, stagnant low price due to recent recession, and drastic change of distribution circumstances. It is requested for us to initiate discussion on such issue as “how fish aquaculture establishes its status in the coastal fishery\ulcorner, will fish aquaculture grow in the future\ulcorner, and if so “how it will be restructured\ulcorner” The above issues can be observed in the mariculture of yellow tail, sea scallop and eel. But there have not been studied concerning seabream even though the production is over 30% of the total production of fish aquaculture in resent and it occupied an important status in the fish aquaculture. The objectives of this study is to forecast the future movement of sea bream aquaculture. The first goal of the study is to contribute to managerial and economic studies on the aquaculture industry. The second goal is to identify the factors influencing the competition between production areas and to identify the mechanisms involved. This study will examine the competitive power in individual producing area, its behavior, and its compulsory factors based on case study. Producing areas will be categorized according to following parameters : distance to market and availability of transportation, natural environment, the time of formation of producing areas (leaderㆍfollower), major production items, scale of business and producing areas, degree of organization in production and sales. As a factor in shaping the production area of sea bream aquaculture, natural conditions especially the water temperature is very important. Sea bream shows more active feeding and faster growth in areas located where the water temperature does not go below 13∼14$^{\circ}C$ during the winter. Also fish aquaculture is constrained by the transporting distance. Aquacultured yellowtail is a mass-produced and a mass-distributed item. It is sold a unit of cage and transported by ship. On the other hand, sea bream is sold in small amount in markets and transported by truck; so, the transportation cost is higher than yellow tail. Aquacultured sea bream has different product characteristics due to transport distance. We need to study live fish and fresh fish markets separately. Live fish was the original product form of aquacultured sea bream. Transportation of live fish has more constraints than the transportation of fresh fish. Death rate and distance are highly correlated. In addition, loading capacity of live fish is less than fresh fish. In the case of a 10 ton truck, live fish can only be loaded up to 1.5 tons. But, fresh fish which can be placed in a box can be loaded up to 5 to 6 tons. Because of this characteristics, live fish requires closer location to consumption area than fresh fish. In the consumption markets, the size of fresh fish is mainly 0.8 to 2kg.Live fish usually goes through auction, and quality is graded. Main purchaser comes from many small-sized restaurants, so a relatively small farmer and distributer can sell it. Aquacultured sea bream has been transacted as a fresh fish in GMS ,since 1993 when the price plummeted. Economies of scale works in case of fresh fish. The characteristics of fresh fish is as follows : As a large scale demander, General Merchandise Stores are the main purchasers of sea bream and the size of the fish is around 1.3kg. It mainly goes through negotiation. Aquacultured sea bream has been established as a representative food in General Merchandise Stores. GMS require stable and mass supply, consistent size, and low price. And Distribution of fresh fish is undertook by the large scale distributers, which can satisfy requirements of GMS. The market share in Tokyo Central Wholesale Market shows Mie Pref. is dominating in live fish. And Ehime Pref. is dominating in fresh fish. Ehime Pref. showed remarkable growth in 1990s. At present, the dealings of live fish is decreasing. However, the dealings of fresh fish is increasing in Tokyo Central Wholesale Market. The price of live fish is decreasing more than one of fresh fish. Even though Ehime Pref. has an ideal natural environment for sea bream aquaculture, its entry into sea bream aquaculture was late, because it was located at a further distance to consumers than the competing producing areas. However, Ehime Pref. became the number one producing areas through the sales of fresh fish in the 1990s. The production volume is almost 3 times the production volume of Mie Pref. which is the number two production area. More conversion from yellow tail aquaculture to sea bream aquaculture is taking place in Ehime Pref., because Kagosima Pref. has a better natural environment for yellow tail aquaculture. Transportation is worse than Mie Pref., but this region as a far-flung producing area makes up by increasing the business scale. Ehime Pref. increases the market share for fresh fish by creating demand from GMS. Ehime Pref. has developed market strategies such as a quick return at a small profit, a stable and mass supply and standardization in size. Ehime Pref. increases the market power by the capital of a large scale commission agent. Secondly Mie Pref. is close to markets and composed of small scale farmers. Mie Pref. switched to sea bream aquaculture early, because of the price decrease in aquacultured yellou tail and natural environmental problems. Mie Pref. had not changed until 1993 when the price of the sea bream plummeted. Because it had better natural environment and transportation. Mie Pref. has a suitable water temperature range required for sea bream aquaculture. However, the price of live sea bream continued to decline due to excessive production and economic recession. As a consequence, small scale farmers are faced with a market price below the average production cost in 1993. In such kind of situation, the small-sized and inefficient manager in Mie Pref. was obliged to withdraw from sea bream aquaculture. Kumamoto Pref. is located further from market sites and has an unsuitable nature environmental condition required for sea bream aquaculture. Although Kumamoto Pref. is trying to convert to the puffer fish aquaculture which requires different rearing techniques, aquaculture technique for puffer fish is not established yet.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.123-128
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• 2017

The purpose of this study was to determine the sodium (Na) and potassium (K) content of seasonal foods Four hundred food samples classified into 8 different kinds of seasonal foods were collected from 150 restaurants located in Seoul. The Na and K content in seasonal foods were the analyzed by ICP-OES. The Average content of Na and K in spicy seafood noodle soup were highest as $391.4{\pm}72.7mg$/100 g and $88.6{\pm}21.4mg$/100 g, respectly, while average content of Na was lowest in spicy buckwheat noodles as $255.3{\pm}67.0mg$/ 100 g and the average content of K was lowest in Udon as $36.3{\pm}17.8mg$/ 100 g, respectively. The Na/K ratio of spicy buckwheat noodles was $3.2{\pm}1.0$, while those of cold buckwheat noodles, fish cake, Udon were very high as measured more than 8. Among the seasonal foods, the average content of Na was highest in Winter group as $366.9{\pm}81.8mg$/ 100 g and the average content of K was highest in Autumn group as $84.8{\pm}24.1mg$/ 100 g. The ratio of Na/K was lowest in Summer group as $5.9{\pm}3.3$.