• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Asia Marketing Journal
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• v.12 no.2
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• pp.1-25
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• 2010

Motion pictures are so typical experience goods that consumers tend to look for more credible information. Hence, movie audiences consider movie viewers' reviews more important than the information provided by the film distributor. Recently many portal sites allow consumers to post their reviews and opinions so that other people check the number of consumer reviews and scores before going to the theater. There are a few previous researches studying the electronic word of mouth(eWOM) effect in the movie industry. They found that the volume of eWOM influenced the revenue of the movie significantly but the valence of eWOM did not affect it much (Liu 2006). The goal of our research is also to investigate the eWOM effects in general. But our research is different from the previous studies in several aspects. First, we study the eWOM effect in Korean movie industry. In other words, we would like to check whether we can generalize the results of the previous research across countries. The similar econometric models are applied to Korean movie data that include 746,282 consumer reviews on 439 movies. Our results show that both the valence(RATING) and the volume(LNMSG) of the eWOM influence weekly movie revenues. This result is different from the previous research findings that the volume only influences the revenue. We conjectured that the difference of self construal between Asian and American culture may explain this difference (Kitayama 1991). Asians including Koreans have more interdependent self construal than American, so that they are easily affected by other people's thought and suggestion. Hence, the valence of the eWOM affects Koreans' choice of the movie. Second, we find the critical defect of the previous eWOM models and, hence, attempt to correct it. The previous eWOM model assumes that the volume of eWOM (LNMSG) is an independent variable affecting the movie revenue (LNREV). However, the revenue can influence the volume of the eWOM. We think that treating the volume of eWOM as an independent variable a priori is too restrictive. In order to remedy this problem, we employed a simultaneous equation in which the movie revenue and the volume of the eWOM can affect each other. That is, our eWOM model assumes that the revenue (LNREV) and the volume of eWOM (LNMSG) have endogenous relationship where they influence each other. The results from this simultaneous equation model showed that the movie revenue and the eWOM volume interact each other. The movie revenue influences the eWOM volume for the entire 8 weeks. The reverse effect is more complex. Both the volume and the valence of eWOM affect the revenue in the first week, but only the volume affect the revenue for the rest of the weeks. In the first week, consumers may be curious about the movie and look for various kinds of information they can trust, so that they use the both the quantity and quality of consumer reviews. But from the second week, the quality of the eWOM only affects the movie revenue, implying that the review ratings are more important than the number of reviews. Third, our results show that the ratings by professional critics (CRATING) had negative effect to the weekly movie revenue (LNREV). Professional critics often give low ratings to the blockbuster movies that do not have much cinematic quality. Experienced audiences who watch the movie for fun do not trust the professionals' ratings and, hence, tend to go for the low-rated movies by them. In summary, applied to the Korean movie ratings data and employing a simultaneous model, our results are different from the previous eWOM studies: 1) Koreans (or Asians) care about the others' evaluation quality more than quantity, 2) The volume of eWOM is not the cause but the result of the revenue, 3) Professional reviews can give the negative effect to the movie revenue.

• 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.

• Science of Emotion and Sensibility
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• v.12 no.4
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• pp.433-442
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• 2009

People are tending to buy products through the Internet rather than purchasing them from the store. Some of the consumers give their feedback on line such as reviews, replies, comments, and blogs after they purchased the products. People are also likely to get some information through the Internet. Therefore, companies and public institutes have been facing this situation where they need to collect and analyze reviews or public opinions for them because many consumers are interested in other's opinions when they are about to make a purchase. However, most of the people's reviews on web site are too numerous, short and redundant. Under these circumstances, the emotion scanning system of text documents on the web is rising to the surface. Extracting writer's opinions or subjective ideas from text exists labeled words like GI(General Inquirer) and LKB(Lexical Knowledge base of near synonym difference) in English, however Korean language is not provided yet. In this paper, we labeled positive, negative, and neutral attribute at 4 POS(part of speech) which are noun, adjective, verb, and adverb in Korean dictionary. We extract construction patterns of emotional words and relationships among words in sentences from a large training set, and learned them. Based on this knowledge, comments and reviews regarding products are classified into two classes polarities with positive and negative using SO-PMI, which found the optimal condition from a combination of 4 POS. Lastly, in the design of the system, a flexible user interface is designed to add or edit the emotional words, the construction patterns related to emotions, and relationships among the words.

• Journal of Plant Biotechnology
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• v.43 no.2
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• pp.151-156
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• 2016

Medicinal plants resources are becoming important assets since their usages have been expanded to the development of functional foods for human health, natural cosmetics, and pharmaceutical industries. However, names are different from each country and their phylogenetic origins are not clear. These lead consumers to be confused. In particular, when they are morphologically similar and distributed as dried roots, it is extremely difficult to differentiate their origins even by specialists. Recently, molecular markers have been extensively applied to identify the origin of many crops. In this review, we tried to overview the current research achievements for the development of suitable 'origin identification' regarding to the differentiation of Angelica species. Furthermore, more advanced techniques including amplification genome based marker analyses are also discussed for their practical applications in the authentication of particular medicinal plant in Angelica species.

• Distribution Business Review
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• no.3
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• pp.155-170
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• 2003

본 연구는 기본적으로 온라인과 오프라인의 통합마케팅을 절명하고 이에 관한 경험적 사례를 개발하기 위한 목적으로 진행되었다. 앨범서비스 영역의 공생적 기반 위에 전개되는 온 -오프라인의 경험적 사례를 개발하고 바람직한 마케팅방향 방향을 제시하였다. 본 연구는 문헌연구와 사례연구를 병행하여 연구를 진행하였다. 사례는 인터넷 기반의 앨범서비스를 제공하기 위한 (주)와이드스쿨이다. 온-오프라인의 협력적 통합마케팅의 전략을 전개하기 위해서는 무엇보다도 온 -오프라인의 뚜렷한 목표를 절정하고 성장방향에 대한 비전을 공유하고 나아가 온 -오프라인의 사명을 감당하는 것이다. 즉, 실천적으로는 \circled1 항상 고객 (customer)기반의 의사결정을 이루며 \circled2 철저한 협력적 돕는 경쟁(competition) 의식과 \circled3 구성원 자신의 일에 대한 자신감(confidence)을 지니고 \circled4 실천을 위한 용기(courage)를 가지고 \circled4 혁신하여 변화(change)를 선도하는 것이다. 온라인(on line)으로 표현되는 인터넷환경은 모든 영역에 변화를 요구하고 있다. 온라인에 관한 연구는 크게 온라인시장의 경쟁(competition)에 관한 연구와, 온라인 소비자(consumer)에 관한 연구 그리고 온라인 시장 참가기업(company)에 관한 연구로 구분된다(이석규 ; 2001). 이중 기업에 관한 연구의 중심에는 e-biz의 수익모텔에 관한 연구가 주류를 이루고 았다(David et al, 1999) 특히 오프라인기업의 경우 어떠한 형태방법으로 온라인 환경에 부응하며 기존의 마케팅활동과 연계할 것인가는 매우 중요한 문제다. 즉 기존의 오프라인기업이 온라인도구변화에 적응하고 이를 전략적으로 활용하기 위해서는 무엇보다도 오프라인과 온라인의 통합에 관한 형태와 전략 등을 명확히 이해하고 적용하는 것이 중요하다. 개수가 감소하는 것과는 상당히 다른 분포이다. 따라서 우리의 관측 결과는 2001년 사자자리 유성우의 극대 시간 전후 2시간에 적어도 0등급 이하의 밝은 유성이 상대적으로 많이 발생하였을 것으로 해석된다. 이런 밝은 유성의 빈도는 유성우 특성 연구에 중요한 의미를 가진다. 그러나 표준성만을 이용해 결정된 유성 등급은 유성의 지속 시간에 대한 불확실성과 전천 카메라 감응도의 비선형성에 의한 불확실성을 내포하고 있음을 지적해 둔다.umn chromatography)를 사용하였고 일련의 정제 과정을 통하여 배양액 중의 L-lactic acid 정제 수율은 약 85% 정도로 나타났으며 HPLC로 분석한 결과 99.7%의 순도를 확인할 수 있었다.경향을 나타내며 유입휫수와 $Dst_{min}$ 사이에는 높은 상관관계(0.83)가 있었다. 둘째, 주상기간 중 자기폭풍의 크기가 클수록 플럭스 비 ($f_{max}/f_{ave}$는 대체로 증가하는 경향을 나타냈다. 그리고 75~113keV 에너지 채널에서의 $Dst_{min}$ 값과 플럭스 비의 상관계수는 0.74로서 가장 높았으며 나머지 에너지 채널 역시 비교적 높은 상관관계를 나타냈다. 셋째, 주상기간 중 총 에너지 유입률 지수와 $Dst_{min}$ 사이에 높은 상관관계가 확인되었다. 특히 환전류를 구성하는 주요 입자의 에너지 영역(75~l13keV)에서 가장 높은(0.80) 상관계수를 기록했다. 넷째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간을 연장시키는 경향을 보이며 큰 자기폭풍일수록 현저했다. 주상에서 관측된 이러한 특성은 서브스톰 확장기 활동이 자기폭풍의 발달과 밀접한 관계가 있음을 시사한다.se that were all low in two aspects, named "the Nonsignificant group".

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.85-105
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• 2012

In recent years, IT advancement has brought out the new Internet communication environment such as online social network services, where people are connected in global network without temporal and spatial limitation. The popular use of online social network helps people share their experience and preference for specific products and services, thus holding large potential to significantly affect firms' business performance through Word-of-Mouth (WOM). This study examines the role of online social network in raising WOM effect on the movie industry by comparing with the similar role of Internet portal, another major online communication channel. Analyzing 109 movies and data from both Twitter and Naver movie, we found that significant WOM effect exists simultaneously in both Twitter and Naver movie. However, we also found that different figures of online viral effects exist depending on the popularity of movies. In the hit movie group, before the movie release, the WOM effect occurs only in Twitter while the WOM effect arises in both Twitter and Naver movie at the same time after the movie release. In the less-popular (or niche) movie group, the WOM effect occurs in both Twitter and Naver movie only before the movie release. Our findings not only deepen theoretical insights into different roles of the two online communication channels in provoking the WOM effect on entertainment products but also provide practitioners with incentive to utilize SNS as strategic marketing platform to enhance their brand reputations.

• Knowledge Management Research
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• v.22 no.3
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• pp.253-271
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• 2021

Due to the spread of COVID-19, the use of O2O order·delivery applications are becoming very common. Unlike the past, where customers could choose the desired transaction method and channel, these days, where customers' choices are very limited, it is urgent to consider the concept of shadow labor which has been hindered by the convenience and the benefits of order·delivery app. To this end, in this study, the service quality factors perceived by users of O2O order·delivery app and their shadow work attributes were identified, and priorities according to their relative importance and satisfaction level were suggested. In order to fulfill research objectives, first, after collecting user reviews for an O2O order·delivery app, the subject words were derived using topic modeling. Research variables were selected by linking 11 keywords with the concepts of previous studies on service quality of mobile apps and those about shadow labor. Eight variables of usefulness, ease of use, stability, design quality, personalization, responsiveness, update, and presence were selected. Based on 32 measurement items from the variables, a revised IPA was conducted, and finally, 'keep', 'concentrate', 'low priority', or 'overkill' service quality factors are revealed.

• Journal of Service Research and Studies
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• v.13 no.2
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• pp.1-23
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• 2023

In this study, we try to discover some success factors, for the entire value chain of the O2O food delivery industry in China, from ordering to delivery. We study the influence of three aspects of the value chain, namely, (1) the mobile platform, (2) the restaurant and food and (3) the delivery service, on the perceived value and repurchase intention of customers. Using structural equation modeling, we develop a structural research model with seven sets of hypotheses relating various independent variable constructs (platform, restaurant, and delivery) and dependent constructs (perceived value and repurchase intention). We find that usefulness of mobile app, the food condition and the availability of offline restaurants were significant antecedents for perceived value and repurchase intention. In addition, fair pricing was a significant antecedent for repurchase intention.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.1-12
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• 2019

The health benefits associated with consumption of fresh produce have been clearly demonstrated and encouraged by international nutrition and health authorities. However, since fresh produce is usually minimally processed, increased consumption of fresh fruits and vegetables has also led to a simultaneous escalation of foodborne illness cases. According to the report by the World Health Organization (WHO), 1 in 10 people suffer from foodborne diseases and 420,000 die every year globally. In comparison to other processed foods, fresh produce can be easily contaminated by various routes at different points in the supply chain from farm to fork. This review is focused on the identification and characterization of possible sources of foodborne illnesses from chemical, biological, and physical hazards and the applicable methodologies to detect potential contaminants. Agro-chemicals (pesticides, fungicides and herbicides), natural toxins (mycotoxins and plant toxins), and heavy metals (mercury and cadmium) are the main sources of chemical hazards, which can be detected by several methods including chromatography and nano-techniques based on nanostructured materials such as noble metal nanoparticles (NMPs), quantum dots (QDs) and magnetic nanoparticles or nanotube. However, the diversity of chemical structures complicates the establishment of one standard method to differentiate the variety of chemical compounds. In addition, fresh fruits and vegetables contain high nutrient contents and moisture, which promote the growth of unwanted microorganisms including bacterial pathogens (Salmonella, E. coli O157: H7, Shigella, Listeria monocytogenes, and Bacillus cereus) and non-bacterial pathogens (norovirus and parasites). In order to detect specific pathogens in fresh produce, methods based on molecular biology such as PCR and immunology are commonly used. Finally, physical hazards including contamination by glass, metal, and gravel in food can cause serious injuries to customers. In order to decrease physical hazards, vision systems such as X-ray inspection have been adopted to detect physical contaminants in food, while exceptional handling skills by food production employees are required to prevent additional contamination.