• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.123-139
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• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Asia Marketing Journal
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• v.12 no.2
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• pp.83-110
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• 2010

Dissatisfied customers may express their dissatisfaction behaviorally. These behavioral responses may impact the firms' profitability. How do we model the impact of self regulatory orientation on emotions and subsequent customer behaviors? Obviously, the positive and negative emotions experienced in these situations will influence the overall degree of satisfaction or dissatisfaction with the service(Zeelenberg and Pieters 1999). Most likely, these specific emotions will also partly determine the subsequent behavior in relation to the service and service provider, such as the likelihood of complaining, the degree to which customers will switch or repurchase, and the extent of word of mouth communication they will engage in(Zeelenberg and Pieters 2004). This study investigates the antecedents, consequences of negative consumption emotion and the moderate effect of attribution processing in an integrated model(self regulatory mode → specific emotions → behavioral responses). We focused on the fact that regret and disappointment have effects on consumer behavior. Especially, There are essentially two approaches in this research: the valence based approach and the specific emotions approach. The authors indicate theoretically and show empirically that it matters to distinguish these approaches in services research. and The present studies examined the influence of two regulatory mode concerns(Locomotion orientation and Assessment orientation) with making comparisons on experiencing post decisional regret and disappointment(Pierro, Kruglanski, and Higgins 2006; Pierro et al. 2008). When contemplating a decision with a negative outcome, it was predicted that high (vs low) locomotion would induce more disappointment than regret, whereas high (vs low) assessment would induce more regret than disappointment. The validity of the measurement scales was also confirmed by evaluations provided by the participating respondents and an independent advisory panel; samples provided recommendations throughout the primary, exploratory phases of the study. The resulting goodness of fit statistics were RMR or RMSEA of 0.05, GFI and AGFI greater than 0.9, and a chi-square with a 175.11. The indicators of the each constructs were very good measures of variables and had high convergent validity as evidenced by the reliability with a more than 0.9. Some items were deleted leaving those that reflected the cognitive dimension of importance rather than the dimension. The indicators were very good measures and had convergent validity as evidenced by the reliability of 0.9. These results for all constructs indicate the measurement fits the sample data well and is adequate for use. The scale for each factor was set by fixing the factor loading to one of its indicator variables and then applying the maximum likelihood estimation method. The results of the analysis showed that directions of the effects in the model are ultimately supported by the theory underpinning the causal linkages of the model. This research proposed 6 hypotheses on 6 latent variables and tested through structural equation modeling. 6 alternative measurements were compared through statistical significance test of the paths of research model and the overall fitting level of structural equation model and the result was successful. Also, Locomotion orientation more positively influences disappointment when internal attribution is high than low and Assessment orientation more positively influences regret when external attribution is high than low. In sum, The results of our studies suggest that assessment and locomotion concerns, both as chronic individual predispositions and as situationally induced states, influence the amount of people's experienced regret and disappointment. These findings contribute to our understanding of regulatory mode, regret, and disappointment. In previous studies of regulatory mode, relatively little attention has been paid to the post actional evaluative phase of self regulation. The present findings indicate that assessment concerns and locomotion concerns are clearly distinct in this phase, with individuals higher in assessment delving more into possible alternatives to past actions and individuals higher in locomotion engaging less in such reflective thought. What this suggests is that, separate from decreasing the amount of counterfactual thinking per se, individuals with locomotion concerns want to move on, to get on with it. Regret is about the past and not the future. Thus, individuals with locomotion concerns are less likely to experience regret. The results supported our predictions. We discuss the implications of these findings for the nature of regret and disappointment from the perspective of their relation to regulatory mode. Also, self regulatory mode and the specific emotions(disappointment and regret) were assessed and their influence on customers' behavioral responses(inaction, word of mouth) was examined, using a sample of 275 customers. It was found that emotions have a direct impact on behavior over and above the effects of negative emotions and customer behavior. Hence, We argue against incorporating emotions such as regret and disappointment into a specific response measure and in favor of a specific emotions approach on self regulation. Implications for services marketing practice and theory are discussed.

• Magazine of the Korean Society of Agricultural Engineers
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• v.16 no.2
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• pp.3361-3394
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• 1974

The purpose of this thesis is to disclose some characteristics of water consumption in relation to the quantities of dry matters through the growing period for two statured varieties of paddy rice which are a tall statured variety and a short one, including the water consumption during seedling period, and to find out the various coefficients of evapotranspiration that are applicable for the water use of an expected yield of the two varieties. PAL-TAL, a tall statured variety, and TONG-lL, a short statured variety were chosen for this investigation. Experiments were performed in two consecutive periods, a seedling period and a paddy field period, In the investigation of seedling period, rectangular galvanized iron evapotranspirometers (91cm${\times}$85cm${\times}$65cm) were set up in a way of two levels (PAL-TAL and TONG-lL varieties) with two replications. A standard fertilization method was applied to all plots. In the experiment of paddy field period, evapotanspiration and evaporation were measured separately. For PAL-TAL variety, the evapotranspiration measurements of 43 plots of rectangular galvanized iron evapotranspirometer (91cm${\times}$85cm${\times}$65cm) and the evaporation measurements of 25 plots of rectangular galvanized iron evaporimeter (91cm${\times}$85cm${\times}$15cm) have been taken for seven years (1966 through 1972), and for TONG-IL variety, the evapotranspiration measurements of 19 plots and the evaporation measurements of 12 plots have been collected for two years (1971 through 1972) with five different fertilization levels. The results obtained from this investigation are summarized as follows: 1. Seedling period 1) The pan evaporation and evapotranspiration during seedling period were proved to have a highly significant correlation to solar radiation, sun shine hours and relative humidity. But they had no significant correlation to average temperature, wind velocity and atmospheric pressure, and were appeared to be negatively correlative to average temperature and wind velocity, and positively correlative to the atmospheric pressure, in a certain period. There was the highest significant correlation between the evapotranspiration and the pan evaporation, beyond all other meteorological factors considered. 2) The evapotranpiration and its coefficient for PAL-TAL variety were 194.5mm and 0.94∼1.21(1.05 in average) respectively, while those for TONG-lL variety were 182.8mm and 0.90∼1.10(0.99 in average) respectively. This indicates that the evapotranspiration for TONG-IL variety was 6.2% less than that for PAL-TAL variety during a seedling period. 3) The evapotranspiration ratio (the ratio of the evapotranspiration to the weight of dry matters) during the seedling period was 599 in average for PAL-TAL variety and 643 for TONG-IL variety. Therefore the ratio for TONG-IL was larger by 44 than that for PAL-TAL variety. 4) The K-values of Blaney and Criddle formula for PAL-TAL variety were 0.78∼1.06 (0.92 in average) and for TONG-lL variety 0.75∼0.97 (0.86 in average). 5) The evapotranspiration coefficient and the K-value of B1aney and Criddle formular for both PAL-TAL and TONG-lL varieties showed a tendency to be increasing, but the evapotranspiration ratio decreasing, with the increase in the weight of dry matters. 2. Paddy field period 1) Correlation between the pan evaporation and the meteorological factors and that between the evapotranspiration and the meteorological factors during paddy field period were almost same as that in case of the seedling period (Ref. to table IV-4 and table IV-5). 2) The plant height, in the same level of the weight of dry matters, for PAL-TAL variety was much larger than that for TONG-IL variety, and also the number of tillers per hill for PAL-TAL variety showed a trend to be larger than that for TONG-IL variety from about 40 days after transplanting. 3) Although there was a tendency that peak of leaf-area-index for TONG-IL variety was a little retarded than that for PAL-TAL variety, it appeared about 60∼80 days after transplanting. The peaks of the evapotranspiration coefficient and the weight of dry matters at each growth stage were overlapped at about the same time and especially in the later stage of growth, the leaf-area-index, the evapotranspiration coefficient and the weight of dry matters for TONG-IL variety showed a tendency to be larger then those for PAL-TAL variety. 4) The evaporation coefficient at each growth stage for TONG-IL and PAL-TALvarieties was decreased and increased with the increase and decrease in the leaf-area-index, and the evaporation coefficient of TONG-IL variety had a little larger value than that of PAL-TAL variety. 5) Meteorological factors (especially pan evaporation) had a considerable influence to the evapotranspiration, the evaporation and the transpiration. Under the same meteorological conditions, the evapotranspiration (ET) showed a increasing logarithmic function of the weight of dry matters (x), while the evaporation (EV) a decreasing logarithmic function of the weight of dry matters; 800kg/10a x 2000kg/10a, ET=al+bl logl0x (bl>0) EV=a2+b2 log10x (a2>0 b2<0) At the base of the weight of total dry matters, the evapotranspiration and the evaporation for TONG-IL variety were larger as much as 0.3∼2.5% and 7.5∼8.3% respectively than those of PAL-TAL variety, while the transpiration for PAL-TAL variety was larger as much as 1.9∼2.4% than that for TONG-IL variety on the contrary. At the base of the weight of rough rices the evapotranspiration and the transpiration for TONG-IL variety were less as much as 3.5% and 8.l∼16.9% respectively than those for PAL-TAL variety and the evaporation for TONG-IL was much larger by 11.6∼14.8% than that for PAL-TAL variety. 6) The evapotranspiration coefficient, the evaporation coefficient and the transpiration coefficient and the transpiration coefficient were affected by the weight of dry matters much more than by the meteorological conditions. The evapotranspiratioa coefficient (ETC) and the evaporation coefficient (EVC) can be related to the weight of dry matters (x) by the following equations: 800kg/10a x 2000kg/10a, ETC=a3+b3 logl0x (b3>0) EVC=a4+b4 log10x (a4>0, b4>0) At the base of the weights of dry matters, 800kg/10a∼2000kg/10a, the evapotranspiration coefficients for TONG-IL variety were 0.968∼1.474 and those for PAL-TAL variety, 0.939∼1.470, the evaporation coefficients for TONG-IL variety were 0.504∼0.331 and those for PAL-TAL variety, 0.469∼0.308, and the transpiration coefficients for TONG-IL variety were 0.464∼1.143 and those for PAL-TAL variety, 0.470∼1.162. 7) The evapotranspiration ratio, the evaporation ratio (the ratio of the evaporation to the weight of dry matters) and the transpiration ratio were highly affected by the meteorological conditions. And under the same meteorological condition, both the evapotranspiration ratio (ETR) and the evaporation ratio (EVR) showed to be a decreasing logarithmic function of the weight of dry matters (x) as follows: 800kg/10a x 2000kg/10a, ETR=a5+b5 logl0x (a5>0, b5<0) EVR=a6+b6 log10x (a6>0 b6<0) In comparison between TONG-IL and PAL-TAL varieties, at the base of the pan evaporation of 343mm and the weight of dry matters of 800∼2000kg/10a, the evapotranspiration ratios for TONG-IL variety were 413∼247, while those for PAL-TAL variety, 404∼250, the evaporation ratios for TONG-IL variety were 197∼38 while those for PAL-TAL variety, 182∼34, and the transpiration ratios for TONG-IL variety were 216∼209 while those for PAL-TAL variety, 222∼216 (Ref. to table IV-23, table IV-25 and table IV-26) 8) The accumulative values of evapotranspiration intensity and transpiration intensity for both PAL-TAL and TONG-IL varieties were almost constant in every climatic year without the affection of the weight of dry matters. Furthermore the evapotranspiration intensity appeared to have more stable at each growth stage. The peaks of the evapotranspiration intensity and transpiration intensity, for both TONG-IL and PAL-TAL varieties, appeared about 60∼70 days after transplanting, and the peak value of the former was 128.8${\pm}$0.7, for TONG-IL variety while that for PAL-TAL variety, 122.8${\pm}$0.3, and the peak value of the latter was 152.2${\pm}$1.0 for TONG-IL variety while that for PAL-TAL variety, 152.7${\pm}$1.9 (Ref.to table IV-27 and table IV-28) 9) The K-value in Blaney & Criddle formula was changed considerably by the meteorological condition (pan evaporation) and related to be a increasing logarithmic function of the weight of dry matters (x) for both PAL-TAL and TONG-L varieties as follows; 800kg/10a x 2000kg/10a, K=a7+b7 logl0x (b7>0) The K-value for TONG-IL variety was a little larger than that for PAL-TAL variety. 10) The peak values of the evapotranspiration coefficient and k-value at each growth stage for both TONG-IL and PAL-TAL varieties showed up about 60∼70 days after transplanting. The peak values of the former at the base of the weights of total dry matters, 800∼2000kg/10a, were 1.14∼1.82 for TONG-IL variety and 1.12∼1.80, for PAL-TAL variety, and at the base of the weights of rough rices, 400∼1000 kg/10a, were 1.11∼1.79 for TONG-IL variety and 1.17∼1.85 for PAL-TAL variety. The peak values of the latter, at the base of the weights of total dry matters, 800∼2000kg/10a, were 0.83∼1.39 for TONG-IL variety and 0.86∼1.36 for PAL-TAL variety and at the base of the weights of rough rices, 400∼1000kg/10a, 0.85∼1.38 for TONG-IL variety and 0.87∼1.40 for PAL-TAL variety (Ref. to table IV-18 and table IV-32) 11) The reasonable and practicable methods that are applicable for calculating the evapotranspiration of paddy rice in our country are to be followed the following priority a) Using the evapotranspiration coefficients based on an expected yield (Ref. to table IV-13 and table IV-18 or Fig. IV-13). b) Making use of the combination method of seasonal evapotranspiration coefficient and evapotranspiration intensity (Ref. to table IV-13 and table IV-27) c) Adopting the combination method of evapotranspiration ratio and evapotranspiration intensity, under the conditions of paddy field having a higher level of expected yield (Ref. to table IV-23 and table IV-27). d) Applying the k-values calculated by Blaney-Criddle formula. only within the limits of the drought year having the pan evaporation of about 450mm during paddy field period as the design year (Ref. to table IV-32 or Fig. IV-22).