• International journal of advanced smart convergence
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• v.9 no.4
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• pp.124-131
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• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.177-181
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• 2023

User reviews posted in the application market show high relevance with the satisfaction of application users and its significance has been proven from numerous studies. User reviews are also crucial data as they are essential for improving applications after its release. However, as infinite amounts of user reviews are posted per day, application developers are unable to examine every user review and address them. Simply addressing the reviews in a chronological order will not be enough for an adequate user satisfaction given the limited resources of the developers. As such, the following research suggests a systematical method of analyzing user reviews with a cost-benefit analysis, in which the benefit of each user review is quantified based on the number of positive/negative words and the cost of each user review is quantified by using function point, a technique that measures software size.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.198-202
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• 2020

User review sites are spaces where users can freely post and share their opinions, which are trusted by many people and directly influence sales. In addition, they overcome the limitations arising from existing requirements collection and are able to gather the needs of large numbers of different people at a low cost. Therefore, such sites are attracting attention as new spaces for understanding user needs. In a previous study, a user review analysis was attempted using 5W and 1H, and we inferred that a sentence containing "when" has special information based on the user experience. In addition, the requirements of the derivative activities in a user review can identify more user needs than the general requirements of derivative activities. In this paper, we propose a systematic method of deriving "when" sentences contain meaningful information from user reviews and converting them into use cases, which is one of the requirements of a specification method. This method converts unstructured data into structured data such that it can be included as the user requirements during software development from user comments expressed in natural language. This method will reduce project failures and increase the likelihood of success by enabling an efficient collection and analysis of user needs from valuable user reviews.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.755-761
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• 2023

As digital transformation accelerates, the proportion of non-face-to-face services in financial services is also increasing. Recently, user experience has emerged to secure competitiveness in mobile services, and analysis techniques to improve user experience have emerged. User review data, one of the data used for quantitative evaluation, contains a lot of unnecessary information, which is time-consuming to derive improvement directions. Therefore, this study aims to develop a UX analysis system based on the hierarchy of UX needs by using a cosine similarity algorithm and analyze user review data of Kookmin Bank, Woori Bank, Kakao Bank, and Toss for verification. This study proved that the developed UX analysis system is a system that can effectively analyze UX through the analysis of user review data. The system of this study is expected to be easily used to identify improvement plans for the hierarchy of UX needs in an agile organization that needs to quickly reflect customer feedback.

• The Journal of Information Systems
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• v.30 no.4
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• pp.227-245
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• 2021

Purpose The purpose of this study is to examine the variables affecting user satisfaction through previous studies and to examine the differences between apps. Differences are based on factors that determine the quality of real estate service apps and derived by the topic modeling results. Design/methodology/approach This study conducts topic modeling to find factors affecting user satisfaction of real estate service apps using user reviews. Sentiment analysis is additionally conduct on the derived topics to examine the user responses. Findings Users give high sentiment scores for services that can manage factors such as usefulness of information, false sales, and hype. In addition, managing the basic services of app is an important factor influencing user satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.457-468
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• 2022

With the rapid growth of the webtoon industry, a new model for webtoon applications has emerged. We have entered the era of webtoon application version 3.0 after ver 1.0 and ver 2.0. Despite these changes, research on user review analysis for webtoon applications is still insufficient. Therefore, this study aims to analyze user reviews for 'Kakao Webtoon (Daum Webtoon)' that presented the webtoon application 3.0 model. For analysis, 20,382 application reviews were collected and pre-processed, and TF-IDF, network analysis, topic modeling, and emotional analysis were conducted for each version. As a result, the user experience of the webtoon application for each version was analyzed and usability testing conducted.

• The Journal of Information Systems
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• v.32 no.4
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• pp.29-50
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• 2023

Purpose The impact of online reviews is widely acknowledged, with extensive research focused on text-based reviews. However, there's a lack of research regarding reviews in video format. To address this gap, this study aims to explore the connection between company-sponsored product review videos and the extent of directive speech within them. This article analyzed viewer sentiments expressed in video comments based on the level of directive speech used by the presenter. Design/methodology/approach This study involved analyzing speech acts in review videos based on sponsorship and examining consumer reactions through sentiment analysis of comments. We used Speech Act theory to perform the analysis. Findings YouTubers who receive company sponsorship for review videos tend to employ more directive speech. Furthermore, this increased use of directive speech is associated with a higher occurrence of negative consumer comments. This study's outcomes are valuable for the realm of user-generated content and natural language processing, offering practical insights for YouTube marketing strategies.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.113-122
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• 2024

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.517-528
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• 2016

This study attempted to explore and examine a new user experience (UX) research method for IoT products which are becoming widely used but lack practical user research. While user experience research has been traditionally opted for survey or observation methods, this paper utilized big data analysis method for user online reviews on an intelligent agent IoT product, Amazon's Echo. The results of topic modelling analysis extracted user experience elements such as features, conversational interaction, and updates. In addition, regression analysis showed that the topic of updates was the most influential determinant of user satisfaction. The main implication of this study is the new introduction of big data analysis method into the user experience research for the intelligent agent IoT products.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.219-231
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• 2023

Intelligent applications are crucial for increasing the popularity of shared urban electric bicycles (EBs). Building an application platform architectural system that can satisfy independent user operations is critical for increasing the intelligent usage of shared EBs. Consequently, we collected online reviews of shared EB applications, conducted semantic processing and sentiment analysis, and refined the positive and negative review data for each function. The positive and negative review indices of each function were calculated using the formulae for positive and negative review indices of product functions, thereby determining the functions that need to be improved. Each function of the Shared EB application was improved according to its business process. The main contributions of this study are to build a user requirement architecture system for the Shared EB application with five dimensions and 22 functions using the Delphi method to design the user interface (UI) of this application based on user satisfaction evaluation results; to create a high-fidelity dynamic interaction prototype and compare user satisfaction before and after improving the Shared EB application functions. The testing results indicate that the changes in the UI significantly improve the user experience satisfaction of the urban Shared EB application, with the positive experience index increasing by 69.21% and the negative experience index decreasing by 75.85% overall. This information can be directly used by relevant companies to improve the functions of the Shared EB application.