• Journal of the Korea Concrete Institute
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• v.19 no.5
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• pp.613-621
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• 2007

Due to the increasing significance of durability, much researches on carbonation, one of the major deterioration phenomena are carried out. However, conventional researches based on fully hardened concrete are focused on prediction of carbonation depth and they sometimes cause errors. In contrast with steel members, behaviors in early-aged concrete such as porosity and hydrates (calcium hydroxide) are very important and may be changed under carbonation process. Because transportation of deteriorating factors is mainly dependent on porosity and saturation, it is desirable to consider these changes in behaviors in early-aged concrete under carbonation for reasonable analysis of durability in long term exposure or combined deterioration. As for porosity, unless the decrease in $CO_2$ diffusion due to change in porosity is considered, the results from the prediction is overestimated. The carbonation depth and characteristics of pore water are mainly determined by amount of calcium hydroxide, and bound chloride content in carbonated concrete is also affected. So Analysis based on test for hydration and porosity is recently carried out for evaluation of carbonation characteristics. In this study, changes in porosity and hydrate $(Ca(OH)_2)$ under carbonation process are performed through the tests. Mercury Intrusion Porosimetry (MIP) for changed porosity, Thermogravimetric Analysis (TGA) for amount of $(Ca(OH)_2)$ are carried out respectively and analysis technique for porosity and hydrates under carbonation is developed utilizing modeling for behavior in early-aged concrete such as multi component hydration heat model (MCHHM) and micro pore structure formation model (MPSFM). The results from developed technique is in reasonable agreement with experimental data, respectively and they are evaluated to be used for analysis of chloride behavior in carbonated concrete.

• Asia pacific journal of information systems
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• v.24 no.3
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• pp.255-279
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• 2014

It is not too much to say that smartphones have become an essential part of our lives due to their versatility. Nevertheless, they still have less overall capabilities than their desktop counterparts. Specifically, they have small screens and low resolutions, which make their applications difficult to have a usable interface. To account for these limitations, the interface of smartphone applications should be designed carefully and properly. Good interface design to any application is critical. However, a comprehensive information systems (IS) literature review found that there has been little research on the user interface design of smartphone applications. More specifically, there has been little empirical evidence and understanding about how metaphors, an imaginative way of describing objects and concepts, in the user interface of smartphone applications affect users' intention to use the applications. Thereby, the research goals of this study are to examine 1) the effects of the metaphors in the user interface of smartphone applications on the interaction between users and applications and 2) the effects of mediating variables including the interaction between users and applications, users' beliefs and attitudes, on users' intention to use the applications. A survey was conducted to collect data. University students and practitioners participated in the survey. A 24-item questionnaire was developed on a 5-point Likert-type scale. The measurement items were mostly adapted from the previous studies in the IS literature and modified to fit the context of this study. First, a principal component factor analysis was performed to explore the inter-relationships among a set of variables. The analysis showed that most of the items loaded quite strongly on the six components. The analysis also revealed the six components with eigenvalues exceeding 1, explaining a total of 70.7 per cent of the variance. The reliabilities of the items were also checked. Most Cronbach alpha values were above 0.8, so the scales were considered reliable. In sum, the results of the analysis support the decision to retain the six factors for further investigation. Next, the structural model was analyzed with AMOS structural equation modeling. The values of GFI, AGFI, NFI, TLI, CFI, and RMSEA were checked. The values showed that the research model considerably have a good fit in general. Next, the convergent and discriminant validities of all constructs were examined. The values for the standardized regression weights and critical ration (CR) indicated sufficient convergent validity for all constructs. In addition, the square root of the average variance extracted (AVE) of each construct was compared with its correlations with all other constructs. The results supported discriminant validity for all constructs. In sum, the results of analysis demonstrated adequate convergent and discriminant validities for all constructs. Finally, path coefficients between the variables were examined. Methphor was found to have an impact on interaction (${\beta}$ = .457, p = .000). There were also significant effects of the interaction on perceived usefulness (${\beta}$ = .273, p = .000) and ease of use (${\beta}$ = .405, p = .000). User attitude was significantly influenced by these two beliefs, perceived usefulness (${\beta}$ = .386, p = .000) and ease of use (${\beta}$ = .347, p = .000) respectively. Further, the results of analysis found that users' intention to use smartphone applications was significantly influenced by user attitude (${\beta}$ = .567, p = .000). Based upon the analyses, all hypotheses were supported. This study found that the metaphors used in the interface of smartphone applications affect not only the interaction between users and applications, but also users' intention to use the applications through the mediating variables, perceived usefulness and ease of use. These findings imply that if the metaphors used in the user interface of application are easy enough to understand for smartphone users, then the application can be perceived useful and easy to use, which in turn make users to have an intention to use the application. In conclusion, this study contributed not only to validate and extend Technology Acceptance Model (TAM) partially, but also to develop the construct of metaphor in smartphone settings. However, since a single empirical study cannot be enough to validate the findings, some limitations should be considered.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.509-516
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• 2004

This study is to investigate the amount of biased estimates for heritability and genetic correlation according to data structure on marbling scores in Korean cattle. Breeding population with 5 generations were simulated by way of selection for carcass weight, Longissimus muscle area and latent values of marbling scores and random mating. Latent variables of marbling scores were categorized into five by the thresholds of 0, I, 2, and 3 SD(DSI) or seven by the thresholds of -2, -1, 0,1I, 2, and 3 SD(DS2). Variance components and genetic pararneters(Heritabilities and Genetic correlations) were estimated by restricted maximum likelihood on multivariate linear mixed animal models and by Gibbs sampling algorithms on multivariate threshold mixed animal models in DS1 and DS2. Simulation was performed for 10 replicates and averages and empirical standard deviation were calculated. Using REML, heritabilitis of marbling score were under-estimated as 0.315 and 0.462 on DS1 and DS2, respectively, with comparison of the pararneter(0.500). Otherwise, using Gibbs sampling in the multivariate threshold animal models, these estimates did not significantly differ to the parameter. Residual correlations of marbling score to other traits were reduced with comparing the parameters when using REML algorithm with assuming linear and normal distribution. This would be due to loss of information and therefore, reduced variation on marbling score. As concluding, genetic variation of marbling would be well defined if liability concepts were adopted on marbling score and implemented threshold mixed model on genetic parameter estimation in Korean cattle.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.19-45
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• 2008

This study is to empirically examine the primary dimensions of visual merchandising (VMD) of internet shopping mall, namely store design, merchandise, and merchandising cues, to be a attractive virtual store to the shoppers. The authors reviewed the literature related to the major components of VMD from the perspective of the AIDA model, which has been mainly applied to the offline store settings. The major purposes of the study are as follows; first, tries to derive the variables related with the components of visual merchandising through reviewing the existing literatures, establish the hypotheses, and test it empirically. Second, examines the relationships between the components of VMD and the attitude toward the VMD, however, putting more emphasis on finding out the component structure of the VMD. VMD needs to be examined with the perspective that an online shopping mall is a virtual self-service or clerkless store, which could reduce the number of employees, help the shoppers search, evaluate and purchase for themselves, and to be explored in terms of the in-store persuasion processes of customers. This study reviewed the literatures related to store design, merchandise, and merchandising cues which might be relevant to the store, product, and promotion respectively. VMD is a total communication tool, and AIDA model could explain the in-store consumer behavior of online shopping. Store design has to do with triggering a consumer attention to the online mall, merchandise with a product related interest, and merchandising cues with promotions such as recommendation and links that induce the desire to pruchase. These three steps might be seen as the processes for purchase actions. The theoretical rationale for the relationship between VMD and AIDA could be found in Tyagi(2005) that the three steps of consumer-oriented merchandising are a store, a product assortment, and placement, in Omar(1999) that three types of interior display are a architectural design display, commodity display, and point-of-sales(POS) display, and in Davies and Ward(2005) that the retail store interior image is related to an atmosphere, merchandise, and in-store promotion. Lee et al(2000) suggested as the web merchandising components a merchandising cues, a shopping metaphor which is an assistant tool for search, a store design, a layout(web design), and a product assortment. The store design which includes differentiation, simplicity and navigation is supposed to be related to the attention to the virtual store. Second, the merchandise dimensions comprising product assortments, visual information and product reputation have to do with the interest in the product offerings. Finally, the merchandising cues that refer to merchandiser(MD)'s recommendation of products and providing the hyperlinks to relevant goods for the shopper is concerned with attempt to induce the desire to purchase. The questionnaire survey was carried out to collect the data about the consumers who would shop at internet shopping malls frequently. To select the subject malls, the mall ranking data announced by a mall rating agency was used to differentiate the most popular and least popular five mall each. The subjects was instructed to answer the questions after navigating the designated mall for five minutes. The 300 questionnaire was distributed to the consumers, 166 samples were used in the final analysis. The empirical testing focused on identifying and confirming the dimensionality of VMD and its subdimensions using a structural equation modeling method. The confirmatory factor analysis for the endogeneous and exogeneous variables was carried out in four parts. The second-order factor analysis was done for a store design, a merchandise, and a merchandising cues, and first-order confirmatory factor analysis for the attitude toward the VMD. The model test results shows that the chi-square value of structural equation is 144.39(d.f 49), significant at 0.01 level which means the proposed model was rejected. But, judging from the ratio of chi-square value vs. degree of freedom, the ratio was 2.94 which smaller than an acceptable level of 3.0, RMR is 0.087 which is higher than a generally acceptable level of 0.08. GFI and AGFI is turned out to be 0.90 and 0.84 respectively. Both NFI and NNFI is 0.94, and CFI 0.95. The major test results are as follows; first, the second-order factor analysis and structural equational modeling reveals that the differentiation, simplicity and ease of identifying current status of the transaction are confirmed to be subdimensions of store design and to be a significant predictors of the dependent variable. This result implies that when designing an online shopping mall, it is necessary to differentiate visually from other malls to improve the effectiveness of the communications of store design. That is, the differentiated store design raise the contrast stimulus to sensory organs to promote the memory of the store and to have a favorable attitude toward the VMD of a store. The results that navigation which means the easiness of identifying current status of shopping affects the attitude to VMD could be interpreted that the navigating processes via the hyperlinks which is characteristics of an internet shopping is a complex and cognitive process and shoppers are likely to lack the sense of overall structure of the store. Consequently, shoppers are likely to be alost amid shopping not knowing where to go. The orientation tool enhance the accessibility of information to raise the perceptive power about the store environment.(Titus & Everett 1995) Second, the primary dimension of merchandise and its subdimensions was confirmed to be unidimensional respectively, have a construct validity, and nomological validity which the VMD dimensions supposed to have a positive correlation with the dependent variable. The subdimensions of product assortment, brand fame and information provision proved to have a positive effect on the attitude toward the VMD. It could be interpreted that the more plentiful the product and brand assortment of the mall is, the more likely the shoppers to favor it. Brand fame and information provision as well affect the VMD attitude, which means that the more famous the brand, the more likely the shoppers would trust and feel familiar with the mall, and the plentifully and visually presented information could have the shopper have a favorable attitude toward the store VMD. Third, it turned out to be that merchandising cue of product recommendation and hyperlinks affect the VMD attitude. This could be interpreted that recommended products could reduce the uncertainty related with the purchase decision, and the hyperlinks to relevant products would help the shopper save the cognitive effort exerted into the information search and gathering, which could lead to a favorable attitude to the VMD. This study tried to sheds some new light on the VMD of online store by reviewing the variables mentioned to be relevant with offline VMD in the existing literatures, and tried to link the VMD components from the perspective of AIDA model. The effect size of the VMD dimensions on the attitude was in the order of the merchandise, the store design and the merchandising cues.It is said that an internet has an unlimited place for display, however, the virtual store is not unlimited since the consumer has a limited amount of cognitive ability to process the external information and internal memory. Particularly, the shoppers are likely to face some difficulties in decision making on account of too many alternative and information overloads. Therefore, the internet shopping mall manager should take into consideration the cost of information search on the part of the consumer, to establish the optimal product placements and search routes. An efficient store composition would be possible by reducing the psychological burdens and cognitive efforts exerted to information search and alternatives evaluation. The store image is in most part determined by the product category and its brand it deals in. The results of this study support this proposition that the merchandise is most important to the VMD attitude than other components, the manager is required to take a strategic approach to VMD. The internet users are getting more accustomed and more knowledgeable about the internet media and more likely to accept the internet as a shopping channel as the period of time during which they use the internet to shop become longer. The web merchandiser should be aware that the product introduction using a moving pictures and a bulletin board become more important in order to present the interactive product information visually and communicate with customers more actively, therefore leading to making the quantity and quality of product information more rich.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.1-31
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• 2008

Trust is central to human relationships, at all times and places. The importance of trust is fundamental in all areas of human life, not only in the area of business administration. 2,500 years ago in China, Confucius taught that the foundation of politics was the trust of the people, more important even than military strength or the supply of food. Shakespeare's play, "Much Ado about Nothing' is about trust and deception. These days, trust and transparency in a commercial organization's business culture form the basis of the 'social capital' by which that organization increases its productivity. A successful company raises productivity by the accumulation of social capital, derived from a trust relationship between business partners, and between the company and consumers. Trust is the crucial factor. At the national level, building trust determines a nation's competitiveness. For a company, long term trust relationships with customers are essential for its survival in a business environment of rapid change. Such relationships, based on trust, are important assets to ensure a company's competitive advantage, and need to be organic to that company's business culture. Because of this importance, trust relationships have been studied in diverse areas within business administration, and especially within marketing, where they form the basis of a successful relationship between producer and consumer. However, what has been lacking is a unified definition of trust. Research has been conducted on the basis of various definitions and models. The majority of researchers have not considered the multidimensional character of the concept of trust until now. Approaches based on a one dimensional model have undermined the value of research results. Furthermore, researchers have only considered trust and trustworthiness as a single component. The majority of research has explored the consequences of perceived trust for outcomes such as loyalty or cooperation, but has neglected the effects of trustworthiness upon the mechanisms of consumer trust. This study focuses on the dimension of trust from such a perspective. It seeks to verify the effect of trust on customer intentions by breaking it down into three separate components: 1) the salesperson, 2) the product/service, and 3) the company. The purposes of this paper are as follows: Firstly, we review the multidimensional nature of trust objects: the salesperson, the product/service, and the company. Secondly, we analyze the relationship between multidimensional trust and trustworthiness. Thirdly, we analyze the connection between trust and repeat purchase intentions for the maintenance of long term relationships. For these purposes the author has developed several hypotheses as follows: H1-1: The competence of a salesperson is positively associated with the trust given by the consumer to the salesperson. H1-2: The benevolence of a salesperson is positively associated with the trust given by the consumer to the salesperson. H2-1: The competence of product/service is positively associated with the trust given by the consumer to the product/service. H2-2: The benevolence of product/service is positively associated with the trust given by the consumer to the product/service. H3-1: The reputation of a company is positively associated with the trust given by the consumer to the company. H3-2: The physical environment of a company is positively associated with the trust given by the consumer to the company. H4-1: Trust in a salesperson is positively associated with repeat purchase intentions. H4-2: Trust in a product/service is positively associated with repeat purchase intentions. H4-3: Trust in a company is positively associated with repeat purchase intentions. The data was compiled from 366 questionnaires. 500 questionnaires were collected, but some of the data was considered unsuitable and inappropriate. The subjects of the survey were male and female customers purchasing products at department stores in Seoul, Daegu and Gyeongbuk. It was carried out between Oct. 25 and 29, 2007. The data was analyzed by frequency analysis using SPSS 12.0 and structural equation modeling using LISREL 8.7. The result of the overall model analysis is as follows: Chi-Square=445.497, d.f.=185, p-value=0.0, GFI=.901, RMSEA=.0617, NNFI=.986, NFI=.981, CFI=.989, AGFI=.864, RMR=.0872. The results of the overall model analysis were coherent. It was found that trust is a multi-dimensional construct, that each of the dimensions of trust are meaningful influences on customer's repurchase intention. Trust in a company may be the most relevant, while trust in a product/service and a salesperson may be less relevant to repurchase intentions. The effective factors in determining trust in a salesperson and a company's product/service were found to be competence and benevolence. Factors in determining trust in a company were its reputation and physical environment, and the relationship of each effective trust factor has been verified in this research. As a result, it was found that competence and benevolence have a meaningful influence on trust in a salesperson and in product/service. It was also found that a company's reputation influences the overall trust in the company significantly but a company's physical environment does not have much effect.

• 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.