• Asian Journal for Public Opinion Research
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• v.2 no.2
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• pp.71-102
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• 2015

This study investigates the effects of information and predisposition on individual responses to hypothetical questions. By employing the empirical implications of theoretical models (EITM) framework, I confirm that information and predisposition have positive effects on individual substantive responses to the hypothetical questions about the independence-unification issue in Taiwan. Respondents with higher levels of information and predisposition are more likely to provide substantive responses. More importantly, information and predisposition exert a negative interaction effect on individual responses to hypothetical questions, which implies that when an individual counts more on information to respond to hypothetical questions, her predisposition plays a less important role in her responses and vice versa. Finally, this study suggests that hypothetical questions are effective to probe individual opinion on specific issues under hypothetical conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3169-3181
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• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• The Journal of Information Systems
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• v.32 no.3
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• pp.151-164
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• 2023

Purpose This study aims to analyze the current status of consumer awareness on Internet banks by conducting a full investigation and collecting user opinions presented on Google Play. After cateogorizing the current dissatisfaction, we would like to present not only the direction of the Internet bank service of but also the improvements of the platform. Design/methodology/approach Using opinion mining, subjectivity analysis, polarity analysis, and polarity information analysis of comments were conducted step by step to extract negative and positive keywords. The extracted keywords analyzed the weights of the frequently appearing positive and negative keywords using the TF-IDF model. Based on previous studies that negative information is more sensitive to positive information, we tried to confirm the connection, proximity, and mediation of negative keywords. Semantic Network Analysis (SNA) was used to visualize the connection relationship between the negative comment keywords of the three Internet banks. Findings Domestic Internet banks such as Kakao Bank, K-Bank, and Toss Bank have attracted a lot of attention even before they were established, and after establishment, they have secured a wide range of users through platforms that are completely different from existing banks. This study found out that the convenience of the app affects the opening and transaction of non-face-to-face accounts, which are characteristics of domestic Internet banks, which also affects the bank's business strategy. In addition, this study shows that the business characteristics of the company can be identified.

• Journal of the Korean Institute of Rural Architecture
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• v.13 no.4
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• pp.107-114
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• 2011

According to the aging society, the housing environments of senior citizens who live alone are faced with social various problems. On the dwelling welfare, development of model for the silver house is necessary at the reducing of social expense. Particularly, the silver housing conditions of rural and fishing villages are poor than urban region. The results of this research are as follows. First, the senior citizens who live alone looked to an negative opinion about cohabitation of the aged, but the senior citizens who don't live alone and preliminary old man group showed a positive opinion to the regarding cohabitation. Second, Most of the aged was in poor health, On this account they expressed an opinion that they were opposite to the cohabitation opinion. Although considering health, simultaneous design of both private life and community life shall be reflected to the preferential design element in co-housing of the aged. Through these co-housing for the aged in rural and fishing village, the senior citizens who lives alone have prevented poor housing surroundings, loneliness, loss of role, uneasiness, gloomy, chronic disease.

• The Journal of Society for e-Business Studies
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• v.19 no.2
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• pp.95-108
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• 2014

Opinion Mining is one of the application domains of text mining which extracting opinions from documents, and much researches are currently underway. Most of related researches focused on the sentiment classification which classifies the documents into positive/negative opinions. However, there is a little interest in extracting the features characterizing the individual product. In this paper, we propose the technique classifying the opinion documents according to the product features, and selecting the those features characterizing each product. In the proposed method, we utilize the document clustering technique and develope a new algorithm for evaluating the similarity between documents. In addition, through experiments, we prove the usefulness of proposed method.

• Hwankyungkyoyuk
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• v.18 no.1 s.26
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• pp.70-81
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• 2005

The importance of classes aiming at enhancing students ability in problem solving and decision making has been being recognized as chances of individual citizen for taking part in social decision making processes. This study was intended to find whether teachers' opinion presentation have effects on students' decision making in a class introducing environmental issues. Total of 6 classes, 202 middle school students have participated in a series of experiments including 4 different environmental issues. Only two issues had been addresses in classes as experimental issues and other two issues not addressed as control issues. For each of the two experimental issues, the teacher researcher applied three different approaches to his students that included positive, negative, or no opinion. The results of this study can be summarized as follows; First, the results showed that students changed their decisions on environmental issues more frequently when dealing with those issues in a class than when not dealing with them. Second, as examining the relationship between patterns in which students make decisions and whether a teacher proposed his opinions or not, it is shown that the rates of students whose opinions is not changed nearly have no difference, while when teachers propose their opinions, it is shown that students who haven't yet chosen their positions easily make their decisions into pros or cons, compared with the opposite case. Third, the results of this study partly supported the third hypothesis that teachers opinion presentation would effect on decision-making of students. It was found that there has been a significant effect in the case of car free day system issue, but no statistically meaningful result in the case of no pets in the national park issue. However, in the issue of car free day system, it seems pretty clear that the students followed the direction of teachers' opinion no matter what it was pros or cons.

• 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.

• Journal of the Society of Disaster Information
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• v.16 no.2
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• pp.343-352
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• 2020

Purpose: This study identifies the impact on composition of the firefighting organization, fire command ability, and public opinion operation on organizational citizenship behavior for fire officials to respond to the earthquake disaster, and provides practical implications as basic data for firefighting organizations to cope with the earthquake disaster. Method: Questionnaire survey was performed for 159 fire officials, and the surveyed data was statistically analyzed by using SPSS 22.0 program. Result: First, the results of the verification of the hypothesis showed that the composition of the fire organization, firefield command ability and public opinion operation have a positive impact on organizational citizenship behavior. Second, the relative contribution of independent variables to the dependent variables was identified in the order of composition of fire organization, fire command ability and public opinion operation. Conclusion: The implications of this study suggested from a practical perspective that the government needs to organize firefighting organizations, develop firefield command ability and operate public opinion in advance in order to respond to earthquakes.

• 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.

• Knowledge Management Research
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• v.25 no.2
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• pp.115-134
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• 2024

In an age where consumers can easily search and pass on their opinions of products and purchasing decisions through the internet, Electronic-word-of-mouth(Ewom) plays an important role in decision making of other potential customers. In this study, we empirically analyze the impact EWOM on consumer purchase decisions, when contradictory Ewom is presented from varying sources of information, such as experts and general consumers. First, we find that when there is a consensus among different information sources there exists a positive relationship between Ewom sentiment and purchase intent, confirming the results of previous literature. However, when expert opinion and consumer opinion do not match we find that consumer opinion is more impactful on purchasing decisions compared to the expert opinion, regardless of product types. The findings of this study add insight to the current literature by examining the effect of contradictory Ewom on purchase decisions, and also to industry marketers by presenting a more efficient strategy in promoting positive Ewom for different product types.