• Asian Journal for Public Opinion Research
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• v.3 no.3
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• pp.145-155
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• 2016

Background: Asia is on track to become the region with the most elderly people in the world. The elderly population will reach 922.7 million by the middle of this century. Therefore, they will be cared for by the today's youth. Negative attitudes toward older people can make adolescents more uncomfortable relating to them. Objectives: The objective of this study was to evaluate validity and reliability of Kogan's attitude toward old people among nursing students in Thailand, Myanmar, and Indonesia. Methods: Content validation of the 34-item Kogan's attitude toward old people was examined by three geriatric nursing experts. The reliability was evaluated on 600 nursing students, from nursing colleges in Thailand (200), Myanmar (200), and Indonesia (200). Findings: The attitude scores towards the elderly ranged from 34 to 238. All of the 34 items were found to have significant item-to-total correlations (p< .05). The reliability results were as follows: In Thailand, Cronbach's alpha was .70 for the total scale, .72 for the positive scale, and .68 for the negative scale. In Myanmar, Cronbach's alpha was .68 for the total scale, .65 for the positive scale, and .66 for the negative scale. In Indonesia, Cronbach's alpha was .66 for the total scale, .71 for the positive scale, and .72 for the negative scale. Conclusions: These findings suggest that Kogan's attitude toward old people is reliable and valid for nursing students from Thailand, Myanmar, and Indonesia. However, Kogan's attitude toward old people should be revised by these countries, including changing wording on all items for better reliability. The revised version must then be tested for reliability and validity.

• Journal of KIISE
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• v.42 no.4
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• pp.512-521
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• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• Health Policy and Management
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• v.19 no.1
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• pp.62-80
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• 2009

The purpose of this study was to examine the factors influencing the attitude toward the increasing role of private health insurance(PHI). In the Korea Welfare Panel Data 2007, a sample of 1,675 (adjusted by weight value: 1,607) respondents on an opinion on promoting PHI was used in the study. With independent variables including socio-demographic characteristics, health status, health-related behavior, and opinions on welfare service, ordered-probit model was used to analyze the attitude toward PHI. Negative opinion on the increasing role of PHI were responded by 54.6%(n=877) of the respondents, whereas 22.2%(n=373) were positive and 23.2%(n=357) were neutral. Old people, the better off, those with worse self-assessed health status, and those having an experience of health examination tend to have the positive attitude toward the increasing role of PHI. Women, those with chronic diseases or disorders and those who do not agree that comprehensive welfare benefits reduce work incentive showed negative attitude toward PHI. When comparing the needs for PHI before and after medical utilization, ex-ante need tends to strengthen the tendency to support private health insurance. This study will contribute to the discussion on the optimal mix of public and private health insurance in Korea by a better recognition of attitude toward PHI and health care system.

• Asian Journal for Public Opinion Research
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• v.11 no.2
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• pp.145-162
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• 2023

This research investigates how users of Bilibili, a video sharing website based in China have responded to carbon neutrality. By conducting quantitative textual analyses on 3,311 comments on Bilibili using LDA topic extraction and content statistics, this research discovers that: (1) Bilibili users have assigned more weight to geopolitical topics (56.3%) than energy (22.0%) and environmental topics (21.7%). (2) When assessing carbon neutrality, Bilibili users considered geopolitical (53.8%) and energy factors (15.8%) more heavily than factors related to the class (9.2%), economy (8.9%), environment (8.7%), and definition (3.6%). (3) More Bilibili users had negative (64.6%) attitudes towards carbon neutrality, with only a small portion of them expressing positive (26.8%) and neutral (8.6%) attitudes. (4) Negative attitudes towards carbon neutrality were mainly driven by geopolitical concerns about the West's approach to China, other countries' free-riding on China's efforts and the West's manipulation of rules, doubts about the feasibility of energy transition and suspicion of capitalists exploiting consumers through this concept. This research highlights the geopolitical concerns behind the environmental attitudes of Chinese people, deepening our understanding to psychological constructs and crisis sensitivity of Chinese people towards environmental issues.

• Human Ecology Research
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• v.58 no.4
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• pp.517-537
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• 2020

This study investigated how consumers and company employees differ in their perceptions of boycotts based on the co-orientation model. Regarding the reasons of boycotts, consumers take both consumer damage cases and unethical conduct equally seriously, while company employees take consumer damage cases more seriously than unethical conduct. Consumer perceptions of the necessity for boycotts was higher than company employees, while employees were more aware of the negative impact of boycotts than consumers. Based on the co-orientation model, we examined how consumers and employees estimate differences in their perceptions of boycotts. The results showed that consumers and company employees are not accurately aware of each other's perceptions. Lastly, logistic regressions were conducted to identify the factors affecting three types of participation: online opinion expression, personal non-purchasing, and persuading other people to join the boycotts. The results showed that male consumers are more likely to participate in online opinion expression; consumer perceptions of effectiveness of boycotts and the perceived severity of consumer damage increase the likelihood of participation in online opinion expression. Consumer perceptions of the necessity of boycotts and their opinion leadership increased the likelihood of non-purchasing. Finally, consumers with higher opinion leadership and female consumers were more likely to encourage others to take part in boycotts. In addition, consumers are more likely to persuade others to join the boycotts if they have stronger beliefs that companies will not seriously consider consumer problems.

• Journal of Korean society of Dental Hygiene
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• v.8 no.4
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• pp.79-88
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• 2008

This study was done to investigate the negative word-of-mouth style and effect of communication with negative word-of-mouth from dental clinic patients. Data were collected from 223 dental clinic patients living in Seoul and GyeongGi-Do. The study was collected from October 15th to October 29th, 2007 with self-recording questionnaires. The results of this study were as follows. First of all, in the characteristic of relationships category, subject who were negative word-of-mouth was more 'persuader person' than others. The results showed that the general characteristics of subjects was effective factor of word-of-mouth. Secondly, the behavior scale which was based of negative word-of-mouth was not suitable of the satisfaction of dental clinic service. This results meant the low satisfaction of dental services haven't relation with negative word-of-mouth. Thirdly, 33% of people who have complaints spread negative word-of-mouth. Finally, the main reason of dissatisfaction was long-waiting time for dental clinic service. The results showed the adjustment of dental clinic system and staffs service will prevent negative word-of-mouth spread.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Asian Journal for Public Opinion Research
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• v.1 no.4
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• pp.307-344
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• 2014

Political parties would normally claim that their campaign and communication materials have effects on voters, be it on their supporters or their opponents during election campaigns. However, such effects are assumed effects by the parties unless voters are themselves assessed about the effects of such materials on themselves. The supporters of the parties are likely to regard such campaign materials as congenial to them but this may not be so with the opposition supporters who would regard such materials as negative. Taking the third-person effect to analyze effects on the audience as the theoretical framework, this study posited that opposition members would regard the materials as negative and thus would claim that they would not have any effect on them but they would likely say that such campaign materials would have effects on own party supporters. Davison (1983) posited that individuals will perceive that negative mediated messages would have their greatest impact not "on me" or "you" but on "them,"- the third person. Research suggests that people judge others to be more influenced than they are by media, advertising, libelous messages, media violence, pornography, and television drama. The theory referred to as the Third-person effect developed on the postulation that audience members would not admit that media had any direct effect on them, but would instead believe that the media influenced others, the third person (Tewksbury, Moy, & Weis, 2004; Price, Tewksbury, & Huang, 1998). On the other hand, while people would discount the effects of negative or biased messages on themselves, they would, under the notion of the First Person Effect, readily admit to being influenced by such messages. This study was based on studying the effects of political literature on party and opposition party supporters taking the messages to be positive to one group and biased and partisan to another group. The study focuses on the assumed effects of political literature on own party and opposition party supporters. It traces the degree of influence of Malaysia's largest political party, Barisan Nasional (BN) political communication literature on its own supporters and on non-BN party supporters. While the third-person effect assumes a null or minimal effect on one's self and some or strong effect on others, the question that arises are on welcoming favorable media effects on oneself and assuming unfavorable effects on others.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• The Journal of Information Systems
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• v.32 no.2
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• pp.177-190
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• 2023

Purpose Recently, as a new business marketing tool, short form content focused on fun and interest has been shared as hashtags. By extracting positive and negative keywords from media audiences through comment analysis of social media news, various stakeholders aim to quickly and easily grasp users' opinions on major news. Design/methodology/approach YouTube videos were searched using the YouTube Data API and the results were collected. Video comments were crawled and implemented as HTML elements, and the collection results were checked on the web page. The collected data consisted of video thumbnails, titles, contents, and comments. Comments were word tokenized with the R program, comparing positive and negative dictionaries, and then quantifying polarity. In addition, social network analysis was conducted using divided positive and negative comments, and the results of centrality analysis and visualization were confirmed. Findings Social media users' opinions on issue news were confirmed by analyzing and visualizing the centrality of keywords through social network analysis by dividing comments into positive and negative. As a result of the analysis, it was found that negative objective reviews had the highest effect on information usefulness. In this way, previous studies have been reaffirmed that online negative information has a strong effect on personal decision-making. Corporate marketers will analyze user comments on social network services (SNS) to detect negative opinions about products or corporate images, which will serve as an opportunity to satisfy customers' needs.