• Journal of the Korean Society of Food Science and Nutrition
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• v.13 no.2
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• pp.215-221
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• 1984

To investigate stability for the quality of selected ginseng products, their sorption properties were clarified in red ginseng extract(RGEP), and red ginseng extract powder(RGEP), and red ginseng extract tea(RGET). Simultaneously, the BET monolayer value of each product was determined in order to inquire out the possibility of establishment as a criterion for the optimum moisture content of the ginseng products. Based on the BET monolayer moisture level of spray dried RGEP which ranged from 4.08 to 4.65%, it would be desirable to establish the optimum moisture content of the products at 4.4${\pm}$0.3%. This is 1.3 to 1.9% lower than the criter on, "less than 6.0%". The optimum moisture level for RGET of which monolayer value ranged 0.93 to 1.37% would be 1.2${\pm}$0.17%. In this case, the maximum permissible limit of moisture content could presumably be raised up to 1.37% in place of current criterion, "less than l.2%". From the results of a study on the growth of molds, the optimum moisture content for RGE assumed to be extended up to 40.0${\pm}$1.07 despite 36.0${\pm}$1.0% of the present criterion. On the other hand, a storage study under the maltreated condition, $48{\pm}2^{\circ}C$ 75%RH, was also carried out in order to make it clear whether the BET monolayer values were able to be used as indices for optimum moisture level of the products. In all samples tested adsorption occurred at even higher levels of moisture than the monolayer values. However, since there are many other possible factors affecting the quality of products the optimum moisture content is preferable to be reduced to the monolayer value. As a result, it was proved that the optimum level of moisture for both RGEP and RGET could be established by the monolayer values.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.55-78
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• 2021

Tosolve complex and diverse social problems and ensure the quality of life of individuals, social robots that can interact with humans are attracting attention. In the past, robots were recognized as beings that provide labor force as they put into industrial sites on behalf of humans. However, the concept of today's robot has been extended to social robots that coexist with humans and enable social interaction with the advent of Smart technology, which is considered an important driver in most industries. Specifically, there are service robots that respond to customers, the robots that have the purpose of edutainment, and the emotionalrobots that can interact with humans intimately. However, popularization of robots is not felt despite the current information environment in the modern ICT service environment and the 4th industrial revolution. Considering social interaction with users which is an important function of social robots, not only the technology of the robots but also other factors should be considered. The design elements of the robot are more important than other factors tomake consumers purchase essentially a social robot. In fact, existing studies on social robots are at the level of proposing "robot development methodology" or testing the effects provided by social robots to users in pieces. On the other hand, consumer emotions felt from the robot's appearance has an important influence in the process of forming user's perception, reasoning, evaluation and expectation. Furthermore, it can affect attitude toward robots and good feeling and performance reasoning, etc. Therefore, this study aims to verify the effect of appearance of social robot and consumer emotions on consumer's attitude toward social robot. At this time, a social robot design evaluation model is constructed by combining heterogeneous data from different sources. Specifically, the three quantitative indicator data for the appearance of social robots from the ABOT Database is included in the model. The consumer emotions of social robot design has been collected through (1) the existing design evaluation literature and (2) online buzzsuch as product reviews and blogs, (3) qualitative interviews for social robot design. Later, we collected the score of consumer emotions and attitudes toward various social robots through a large-scale consumer survey. First, we have derived the six major dimensions of consumer emotions for 23 pieces of detailed emotions through dimension reduction methodology. Then, statistical analysis was performed to verify the effect of derived consumer emotionson attitude toward social robots. Finally, the moderated regression analysis was performed to verify the effect of quantitatively collected indicators of social robot appearance on the relationship between consumer emotions and attitudes toward social robots. Interestingly, several significant moderation effects were identified, these effects are visualized with two-way interaction effect to interpret them from multidisciplinary perspectives. This study has theoretical contributions from the perspective of empirically verifying all stages from technical properties to consumer's emotion and attitudes toward social robots by linking the data from heterogeneous sources. It has practical significance that the result helps to develop the design guidelines based on consumer emotions in the design stage of social robot development.

• Journal of Distribution Research
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• v.17 no.2
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• pp.63-92
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• 2012

This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model in the present study as a conceptual framework. The results of an online survey of 335 mobile phone users in the U.S. indicated the positive effects of arousal and information relevancy on pleasure. In addition, there was a significant relationship between pleasure and intention to use a LBMSS. However, the relationship between dominance and pleasure was not statistically significant. The results of the present study provides insight to retailers and marketers as to what factors they need to consider to implement location-based mobile shopping services to improve their business performance. Extended Abstract : Location aware technology has expanded the marketer's reach by reducing space and time between a consumer's receipt of advertising and purchase, offering real-time information and coupons to consumers in purchasing situations (Dickenger and Kleijnen, 2008; Malhotra and Malhotra, 2009). LBMSS increases the relevancy of SMS marketing by linking advertisements to a user's location (Bamba and Barnes, 2007; Malhotra and Malhotra, 2009). This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective response. The purpose of the study was to examine the relationship among information relevancy and affective variables and their effects on intention to use LBMSS. Thus, information relevancy was integrated into pleasure-arousal-dominance (PAD) model and generated the following hypotheses. Hypothesis 1. There will be a positive influence of arousal concerning LBMSS on pleasure in regard to LBMSS. Hypothesis 2. There will be a positive influence of dominance in LBMSS on pleasure in regard to LBMSS. Hypothesis 3. There will be a positive influence of information relevancy on pleasure in regard to LBMSS. Hypothesis 4. There will be a positive influence of pleasure about LBMSS on intention to use LBMSS. E-mail invitations were sent out to a randomly selected sample of three thousand consumers who are older than 18 years old and mobile phone owners, acquired from an independent marketing research company. An online survey technique was employed utilizing Dillman's (2000) online survey method and follow-ups. A total of 335 valid responses were used for the data analysis in the present study. Before the respondents answer any of the questions, they were told to read a document describing LBMSS. The document included definitions and examples of LBMSS provided by various service providers. After that, they were exposed to a scenario describing the participant as taking a saturday shopping trip to a mall and then receiving a short message from the mall. The short message included new product information and coupons for same day use at participating stores. They then completed a questionnaire containing various questions. To assess arousal, dominance, and pleasure, we adapted and modified scales used in the previous studies in the context of location-based mobile shopping service, each of the five items from Mehrabian and Russell (1974). A total of 15 items were measured on a seven-point bipolar scale. To measure information relevancy, four items were borrowed from Mason et al. (1995). Intention to use LBMSS was captured using two items developed by Blackwell, and Miniard (1995) and one items developed by the authors. Data analyses were conducted using SPSS 19.0 and LISREL 8.72. A total of usable 335 data were obtained after deleting the incomplete responses, which results in a response rate of 11.20%. A little over half of the respondents were male (53.9%) and approximately 60% of respondents were married (57.4%). The mean age of the sample was 29.44 years with a range from 19 to 60 years. In terms of the ethnicity there were European Americans (54.5%), Hispanic American (5.3%), African-American (3.6%), and Asian American (2.9%), respectively. The respondents were highly educated; close to 62.5% of participants in the study reported holding a college degree or its equivalent and 14.5% of the participants had graduate degree. The sample represents all income categories: less than $24,999 (10.8%), $25,000-$49,999 (28.34%), $50,000-$74,999 (13.8%), and $75,000 or more (10.23%). The respondents of the study indicated that they were employed in many occupations. Responses came from all 42 states in the U.S. To identify the dimensions of research constructs, Exploratory Factor Analysis (EFA) using a varimax rotation was conducted. As indicated in table 1, these dimensions: arousal, dominance, relevancy, pleasure, and intention to use, suggested by the EFA, explained 82.29% of the total variance with factor loadings ranged from .74 to .89. As a next step, CFA was conducted to validate the dimensions that were identified from the exploratory factor analysis and to further refine the scale. Table 1 exhibits the results of measurement model analysis and revealed a chi-square of 202.13 with degree-of-freedom of 89 (p =.002), GFI of .93, AGFI = .89, CFI of .99, NFI of .98, which indicates of the evidence of a good model fit to the data (Bagozzi and Yi, 1998; Hair et al., 1998). As table 1 shows, reliability was estimated with Cronbach's alpha and composite reliability (CR) for all multi-item scales. All the values met evidence of satisfactory reliability in multi-item measure for alpha (>.91) and CR (>.80). In addition, we tested the convergent validity of the measure using average variance extracted (AVE) by following recommendations from Fornell and Larcker (1981). The AVE values for the model constructs ranged from .74 through .85, which are higher than the threshold suggested by Fornell and Larcker (1981). To examine discriminant validity of the measure, we again followed the recommendations from Fornell and Larcker (1981). The shared variances between constructs were smaller than the AVE of the research constructs and confirm discriminant validity of the measure. The causal model testing was conducted using LISREL 8.72 with a maximum-likelihood estimation method. Table 2 shows the results of the hypotheses testing. The results for the conceptual model revealed good overall fit for the proposed model. Chi-square was 342.00 (df = 92, p =.000), NFI was .97, NNFI was .97, GFI was .89, AGFI was .83, and RMSEA was .08. All paths in the proposed model received significant statistical support except H2. The paths from arousal to pleasure (H1: ${\ss}$=.70; t = 11.44), from information relevancy to intention to use (H3 ${\ss}$ =.12; t = 2.36), from information relevancy to pleasure (H4 ${\ss}$ =.15; t = 2.86), and pleasure to intention to use (H5: ${\ss}$=.54; t = 9.05) were significant. However, the path from dominance to pleasure was not supported. This study investigated consumer intention to use a location-based mobile shopping service (LBMSS) that integrates cognitive and affective responses. Information relevancy was integrated into pleasure-arousal-dominance (PAD) emotional state model as a conceptual framework. The results of the present study support previous studies indicating that emotional responses as well as cognitive responses have a strong impact on accepting new technology. The findings of this study suggest potential marketing strategies to mobile service developers and retailers who are considering the implementation of LBMSS. It would be rewarding to develop location-based mobile services that integrate information relevancy and which cause positive emotional responses.