• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.392-403
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• 2008

User reviews are valuable information that can be used for various purposes. In particular, the product reviews on online shopping sites are important information which can directly affect the purchasing decision of the customers. In this paper, we present our design and implementation of a system for summarizing the customer's opinion and the features of each product by analyzing reviews on a commercial shopping site. During the analysis process, several natural language processing(NLP) techniques and the semantic dictionary were used. The semantic dictionary contains vocabularies that are used to express product features and customer's opinions. And it was constructed in semi-automatic way with the help of the tool we implemented. Furthermore, we discuss how to handle the vocabularies that have different meanings according to the context. We analyzed 1796 reviews about 20 products of 2 categories collected from an actual shopping site and implemented a novel ranking system. We obtained 88.94% for precision and 47.92% for recall on extracting opinion expression, which means our system can be applicable for real use.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.3
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• pp.129-137
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• 2018

VOCs have been used as the most definitive resource to reflect customer feedback when developing products and services. However, due to the development of the Internet and the emergence of SNS, VOC is no longer the only channel that represents customer opinions. There are also a number of studies showing that many customers express complaints through channels other than VOCs. In this paper, we analyze the difference between the official VOC data and the data collected through the external channel, and suggest ways to reflect the various opinions of customers. To do this, this study uses keyword analysis that can identify differences according to frequency through social network, modular analysis to distinguish topics according to centrality and similarity, and emotional analysis to confirm word polarity (positive and negative). The results of this study show that the opinions of the customers were different depending on channels such as VOCs and external channels. Therefore, the collected data through VOC as well as external channels should be used in order to reflect the opinions of customers. In particular, this paper confirms that the results of one channel may vary depending on the channel characteristics even for the same channel. This confirms that collecting voc only on certain channels may differ from what real customers require. Therefore, data collected through VOCs as well as external channels must be used to reflect various customer feedback.

• The Journal of Information Systems
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• v.25 no.1
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• pp.75-104
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• 2016

Purpose The business ecosystem perspective offers a new lens in which to view customers. Customers as the member of business ecosystems influence firms by participating in both the firm level activities and the business ecosystem level activities. For example, customers participate in the business ecosystems by forming interest groups, allowing their voice to be heard the within business ecosystems. Customers can also, turn public opinion around and foster the business ecosystems favorable to firms. On the other hand, as an extreme case of customer participation, customers can engage in community activities to boycott the purchase of products or services from certain firms or business ecosystems. Design/methodology/approach This study views content creation and feedback activities as customer participation in the firm level. On the other hand, word-of-mouth (WOM) and boycott activities are considered as customer participation in the business ecosystem level. This study presents a research model regarding the relationships among customer socialization, customer participation, and psychological ownership. The proposed model is validated through an empirical analysis on online platform business ecosystems. Findings When the two business ecosystems are compared, different results were drawn. In the Facebook ecosystem, boycott and psychological ownership did not have a significant relationship. However, in the Kakao ecosystem, the two had a significant positive relationship. The mediating effect of the business ecosystem type sheds a light on the mission, purpose, vision, and other values associated with the theory of the business on the customer-firm relationship. Further implications for theory and practice were discussed in this study.

• Journal of Information Technology Services
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• v.15 no.1
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• pp.197-210
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• 2016

With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.

• The Journal of the Acoustical Society of Korea
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• v.11 no.1
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• pp.14-21
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• 1992

In order to establish the speech transmission quality of networks, a series of subjective tests for loudness rating(LR) and sidetone masking rating(STMR) among transmission impairments were carried out. As a result of subjective tests, relationships of mean opinion score(MOS) with LR and STMR, respectively, were obtained. Also, we obtained the cumulative MOS characteristics which represent the percentage of scores that subjects voted. Thus it is easy to achieve a strategic objective of customer satisfaction for present networks and new services.

• The Journal of Information Systems
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• v.20 no.2
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• pp.21-36
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• 2011

The Web has become an excellent source for gathering consumer opinions. There are now numerous Web sites containing such opinions, e.g., customer reviews of products, forums, discussion groups, and blogs. This paper focuses on online customer reviews of products. It makes some contributions. Especially it proposes minimalism and chunking framework for analyzing and comparing consumer opinions of competing products. Users are able to clearly see the strengths and weaknesses of each product in the minds of consumers in terms of various product features. This comparison is useful to both potential customers and product manufacturers. For a product manufacturer, the comparison enables it to easily gather marketing intelligence and product benchmarking information. In this paper, we only focus on mining opinion/product features that the reviewers have commented on. Five types of online review presentations are presented to mine such features. Our experimental results show that these techniques are useful to identify customers' opinions and trends.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.95-100
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• 2020

Among the customer-oriented data used to comprehend the customer, the user review data has received much attention as it provides insights into customer opinion in a detailed and large-scale manner; many customers have come to rely upon and trust the user reviews. Many application developers are cognizant of the importance of user reviews, so they monitor and respond to these reviews. However, due to the absence of a systematic method, developers have been investing their time and money without clear correlation to the customer satisfaction. Therefore, this paper suggests a systematic method to select user reviews from the application market using the Kano Model that deals with customer satisfaction and service quality, thereby maximizing the customer satisfaction under the given time period and budget. This method is constructed in the following phases: the user review collection and requirement elicitation phase in which the developers collect user reviews from the application market and elicit requirements, the Kano Model application and selection phase in which the Kano Model is applied to the elicited requirements and selection occurs based on the quality type, and the stakeholder review and redefinition phase in which relevant personnel gather to review and redefine requirements from an internal perspective.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.99-102
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• 2006

We recognize call centers as a interface frequently interacting with clients, and as the first portal of enterprises promoting customer satisfaction and increasing the rate of customer maintenance. The importance of service quality in call centers is gradually enlarged, as criteria for competitive power of enterprises, and the first-line interface of communications with customers in operating method and business management. Also, Enhancing service quality is the first task of both the management and telemarketers in order to adapt to the customer's requirement level. The curriculum for telemarketer education is not established or standardized within the country yet. We must therefore study on it as soon as possible, though it has a short history and insufficient theoretics. In this paper, we descirbe the development of a formal telemarketer education program in call centers included the result that analyze existing educational programs, and the opinion of call centers.