• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Applied Biological Chemistry
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• v.7
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• pp.105-117
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• 1966

This experiment was conducted to investigate the effet of phthalimido-methyl-O,O-dimethyl-phosphorodithioate (Imidan) known as an acaricide and its possible metabolic products on the growth of plant, when sprayed on the leaves of rice plant. The results are summarized as follows. 1) Possible metabolic products of Imidan, the following compounds were synthesized or recrystallized for the present experiment a) N-Hydroxymethyl phthalimidem b) Phthalimide c) Phthalamidic acid d) Phthalic acid e) Anthranilic acid f) p-Amino benzoic acid g) p-Hydroxy benzoic acid h) Benzoic acid 2) Among the above materials, a), c), d), e), and Imidan were dissolved in a buffer solution respectively to be 10 and 20 p.p.m. and tested with the wheat coleoptile straight growth method. According to the results, Imidan inhibited the growth of coleoptile in both 10 and 20 p.p.m., whereas the others showed much better growth than the control, especially phthalamidic acid in 10 p.p.m. It appears that Imidan itself inhibits the coleoptile growth, whereas the metabolites derived from Imidan through various metabolisms, including hydrolysis in plant tissues show growth-regulating activity. (refer: Table 1, Fig. 1) 3) 20, 100 and 200 p.p.m. solutions of Imidall emulsion in xylene f·ere prepared. The lengths of shoot and root of rice seeds germinated on the re-respective media were measured after 12 days. The data showed that root was much more elongated in Imidan 20 p.p.m., whereas shoot in Imidan 100 p.p.m., respectively, than in the xylene control. An interesting finding was that xylene used as solvent had a tendency to inhibit seriously the root growth of rice seed. (refer: Table 2,5). 4) The emulsions of concentrations in 10, 25, 50 and 100 p.p.m's of control, Imidan, N-hydroxy methyl phthalimide, anthranilic acid, and phthalmide, respectively, were sprayed twice on the rice plant on pot. After a certain period of time lengths of rice culms were measured, showing that plots treated with Imidan and N-hydroxy methyl phthalimide exhibited much more growth than those of control and the others. 5) Loaves and stems of rice plant were sampled and extracted with dried acetone at the intervals of 3-, 5-, 7-, and 14 days after treated with Imidan 250 p.p.m. emulsion. This sample extracted with acetone was purified by means of prechromatographic purification method with acetonitrile and paperchromatographed to detect the following metabolic products. Imidan (Rf: 0.97-0,98), N-hydroxy-methyl phthalimide (Rf: 0.87) phthalimide (Rf: 0.86-0.87), phthalamidic acid (Rf: 0.13-0.14), phthalic acid (Rf: 0.02-0.03), benzoic acid (Rf: 0.42-0.43), p-amino benzoic acid or p-hydroxy benzoic acid (Rf: 0.08-0.09), and unidentified compounds (Rf: 0.73, 0.59, 0.33, 0.23. 0.07). In addition, in the early stages, such as 3- and 5 days nonhydrolyzed Imidan and its first hydrolytic product, N-hydroxymethyl phthalimide were detected in relatively large amounts, whereas in the last stages of 7- and 14 days due to further decomposition, the afore-mentioned two materials were reduced in the amount and p-hthalic, phthalamidic, benzoic, and p-Hydroxy benzoic, or p-Amino benzoic acids were detected in a considerably large amount. It is, therefore, believed that most of Imidan applied to the leaves of rice plant may be decomposed within almost 14 days. In the light of above observations it is considered that Imidan itself is not involved in plant growth regulating activity, whereas various phthaloyl derivatives produced in the course of metabolism (namelr, enzymic action) in plant tissues may have such effect.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Distribution Science
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• v.10 no.3
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• pp.51-61
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• 2012

This study seeks to understand how the choice of a coffee shop is related to a customer's loyalty and which characteristics of a shop influence this choice. It considers large-sized coffee shops brands whose market scale has gradually grown. The users' choice of shop is determined by price, employee service, shop location, and shop atmosphere. The study investigated the effects of these four properties on the brand attitudes of coffee shops. The effects were found to vary depending on users' characteristics. The properties with the largest influence were shop atmosphere and shop location Therefore, the purpose of the study was to examine the properties that could help coffee shops get loyal customers, and the choice properties that could satisfy consumers' desires The study examined consumers' perceptions of shop properties at selection of coffee shop and the difference between perceptual difference and coffee brand in order to investigate customers' desires and needs and to suggest ways that could supply products and service. The research methodology consisted of two parts: normative and empirical research, which includes empirical analysis and statistical analysis. In this study, a statistical analysis of the empirical research was carried out. The study theoretically confirmed the shop choice properties by reviewing previous studies and performed an empirical analysis including cross tabulation based on secondary material. The findings were as follows: First, coffee shop choice properties varied by gender. Price advantage influenced the choice of both men and women; men preferred nearer coffee shops where they could buy coffee easily and more conveniently than women did. The atmosphere of the coffee shop had the greatest influence on both men and women, and shop atmosphere was thought to be the most important for age analysis. In the past, customers selected coffee shops solely to drink coffee. Now, they select the coffee shop according to its interior, menu variety, and atmosphere owing to improved quality and service of coffee shop brands. Second, the prices of the brands did not vary much because the coffee shops were similarly priced. The service was thought to be more important and to elevate service quality so that price and employee service and other properties did not have a great influence on shop choice. However, those working in the farming, forestry, fishery, and livestock industries were more concerned with the price than the shop atmosphere. College and graduate school students were also affected by inexpensive price. Third, shop choice properties varied depending on income. The shop location and shop atmosphere had a greater influence on shop choice. The customers in an income bracket of less than 2 million won selected low-price coffee shops more than those earning 6 million won or more. Therefore, price advantage had no relation with difference in income. The higher income group was not affected by employee service. Fourth, shop choice properties varied depending on place. For instance, customers at Ulsan were the most affected by the price, and the ones at Busan were the least affected. The shop location had the greatest influence among all of the properties. Among the places surveyed, Gwangju had the least influence. The alternate use of space in a coffee shop was thought to be important in all the cities under consideration. The customers at Ulsan were not affected by employee service, and they selected coffee shops according to quality and preference of shop atmosphere. Lastly, the price factor was found to be a little higher than other factors when customers frequently selected brands according to shop properties. Customers at Gwangju reacted to discounts more than those in other cities did, and the former gave less priority to the quality and taste of coffee. Brand preference varied depending on coffee shop location. Customers at Busan selected brands according to the coffee shop location, and those at Ulsan were not influenced by employee kindness and specialty. The implications of this study are that franchise coffee shop businesses should focus on customers rather than aggressive marketing strategies that increase the number of coffee shops. Thus, they should create an environment with a good atmosphere and set up coffee shops in places that customers have good access to. This study has some limitations. First, the respondents were concentrated in metropolitan areas. Secondary data showed that the number of respondents at Seoul was much more than that at Gyeonggi-do. Furthermore, the number of respondents at Gyeonggi-do was much more than those at the six major cities in the nation. Thus, the regional sample was not representative enough of the population. Second, respondents' ratio was used as a measurement scale to test the perception of shop choice properties and brand preference. The difficulties arose when examining the relation between these properties and brand preference, as well as when understanding the difference between groups. Therefore, future research should seek to address some of the shortcomings of this study: If the coffee shops are being expanded to local areas, then a questionnaire survey of consumers at small cities in local areas shall be conducted to collect primary material. In particular, variables of the questionnaire survey shall be measured using Likert scales in order to include perception on shop choice properties, brand preference, and repurchase. Therefore, correlation analysis, multi-regression, and ANOVA shall be used for empirical analysis and to investigate consumers' attitudes and behavior in detail.

• MISULJARYO - National Museum of Korea Art Journal
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• v.98
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• pp.226-241
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• 2020

Located on the right side of the third floor of the State Hermitage Museum in St. Petersburg, the "Art of Central Asia" exhibition boasts the world's finest collection of artworks and artifacts from the Silk Road. Every item in the collection has been classified by region, and many of them were collected in the early twentieth century through archaeological surveys led by Russia's Pyotr Kozlov, Mikhail Berezovsky, and Sergey Oldenburg. Some of these artifacts have been presented around the world through special exhibitions held in Germany, France, the United Kingdom, the Netherlands, Korea, Japan, and elsewhere. The fruits of Russia's Silk Road expeditions were also on full display in the 2008 exhibition The Caves of One Thousand Buddhas - Russian Expeditions on the Silk Route on the Occasion of 190 Years of the Asiatic Museum, held at the Hermitage Museum. Published in 2018 by the Shanghai Chinese Classics Publishing House in collaboration with the Hermitage Museum, Kuche Art Relics Collected in Russia introduces the Hermitage's collection of artifacts from the Kuche (or Kucha) region. While the book focuses exclusively on artifacts excavated from the Kuche area, it also includes valuable on-site photos and sketches from the Russian expeditions, thus helping to enhance readers' overall understanding of the characteristics of Kuche art within the Buddhist art of Central Asia. The book was compiled by Dr. Kira Samosyuk, senior curator of the Oriental Department of the Hermitage Museum, who also wrote the main article and the artifact descriptions. Dr. Samosyuk is an internationally renowned scholar of Central Asian Buddhist art, with a particular expertise in the art of Khara-Khoto and Xi-yu. In her article "The Art of the Kuche Buddhist Temples," Dr. Samosyuk provides an overview of Russia's Silk Road expeditions, before introducing the historical development of Kuche in the Buddhist era and the aspects of Buddhism transmitted to Kuche. She describes the murals and clay sculptures in the Buddhist grottoes, giving important details on their themes and issues with estimating their dates, and also explains how the temples operated as places of worship. In conclusion, Dr. Samosyuk argues that the Kuche region, while continuously engaging with various peoples in China and the nomadic world, developed its own independent Buddhist culture incorporating elements of Gandara, Hellenistic, Persian, and Chinese art and culture. Finally, she states that the culture of the Kuche region had a profound influence not only on the Tarim Basin, but also on the Buddhist grottoes of Dunhuang and the central region of China. A considerable portion of Dr. Samosyuk's article addresses efforts to estimate the date of the grottoes in the Kuche region. After citing various scholars' views on the dates of the murals, she argues that the Kizil grottoes likely began prior to the fifth century, which is at least 100 years earlier than most current estimates. This conclusion is reached by comparing the iconography of the armor depicted in the murals with related materials excavated from the surrounding area (such as items of Sogdian art). However, efforts to date the Buddhist grottoes of Kuche must take many factors into consideration, such as the geological characteristics of the caves, the themes and styles of the Buddhist paintings, the types of pigments used, and the clothing, hairstyles, and ornamentation of the depicted figures. Moreover, such interdisciplinary data must be studied within the context of Kuche's relations with nearby cultures. Scientific methods such as radiocarbon dating could also be applied for supplementary materials. The preface of Kuche Art Relics Collected in Russia reveals that the catalog is the first volume covering the Hermitage Museum's collection of Kuche art, and that the next volume in the series will cover a large collection of mural fragments that were taken from Berlin during World War II. For many years, the whereabouts of these mural fragments were unknown to both the public and academia, but after restoration, the fragments were recently re-introduced to the public as part of the museum's permanent exhibition. We look forward to the next publication that focuses on these mural fragments, and also to future catalogs introducing the artifacts of Turpan and Khotan. Currently, fragments of the murals from the Kuche grottoes are scattered among various countries, including Russia, Germany, and Korea. With the publication of this catalog, it seems like an opportune time to publish a comprehensive catalog on the murals of the Kuche region, which represent a compelling mixture of East-West culture that reflects the overall characteristics of the region. A catalog that includes both the remaining murals of the Kizil grottoes and the fragments from different parts of the world could greatly enhance our understanding of the murals' original state. Such a book would hopefully include a more detailed and interdisciplinary discussion of the artifacts and murals, including scientific analyses of the pigments and other materials from the perspective of conservation science. With the ongoing rapid development in western China, the grotto murals are facing a serious crisis related to climate change and overcrowding in the oasis city of Xinjiang. To overcome this challenge, the cultural communities of China and other countries that possess advanced technology for conservation and restoration must begin working together to protect and restore the murals of the Silk Road grottoes. Moreover, centers for conservation science should be established to foster human resources and collect information. Compiling the data of Russian expeditions related to the grottoes of Kuche (among the results of Western archaeological surveys of the Silk Road in the early twentieth century), Kuche Art Relics Collected in Russia represents an important contribution to research on Kuche's Buddhist art and the Silk Road, which will only be enhanced by a future volume introducing the mural fragments from Germany. As the new authoritative source for academic research on the artworks and artifacts of the Kuche region, the book also lays the groundwork for new directions for future studies on the Silk Road. Finally, the book is also quite significant for employing a new editing system that improves its academic clarity and convenience. In conclusion, Dr. Kira Samosyuk, who planned the publication, deserves tremendous praise for taking the research of Silk Road art to new heights.