• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.125-137
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• 2011

Product reviews in online shopping sites can serve as a useful guideline to buying decisions of customers. However, due to the massive amount of such reviews, it is almost impossible for users to read all the product reviews. For this reason, e-commerce sites provide users with useful reviews or statistics of ratings on products that are manually chosen or calculated. Opinion mining or sentiment analysis is a study on automating above process that involves firstly analyzing users' reviews on a product to tell if a review contains positive or negative feedback, and secondly, providing a summarized report of users' opinions. Previous researches focus on either providing polarity of a user's opinion or summarizing user's opinion on a feature of a product that result in relatively low usage of information that a user review contains. Actual user reviews contains not only mere assessment of a product, but also dissatisfaction and flaws of a product that a user experiences. There are increasing needs for effective analysis on such criteria to help users on their decision-making process. This paper proposes a model that stores various types of user reviews in a data warehouse, and analyzes integrated reviews dynamically. Also, we analyze reviews of an online application shopping site with the proposed model.

• 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.

• Journal of Information Technology Applications and Management
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• v.31 no.3
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• pp.93-104
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• 2024

This study conducts an in-depth analysis of user reviews from three leading metaverse platforms - Minecraft, Roblox, and Zepeto - using advanced topic modeling techniques to uncover key factors for business success. By examining a substantial dataset of user feedback, we identified and categorized the main themes and concerns expressed by users. Our analysis revealed that common issues across all platforms include technical functionality problems, user engagement and interest, payment concerns, and connection difficulties. Specifically, Minecraft users highlighted the importance of adventure and creativity, Roblox users expressed significant concerns about security and fraud, and Zepeto users focused heavily on the fairness of the in-game economy. The findings suggest that for metaverse platforms to achieve sustained success, they must prioritize the resolution of technical issues, enhance features that foster user engagement, ensure reliable connectivity, and address platform-specific concerns such as security for Roblox and payment fairness for Zepeto. These insights provide valuable guidance for developers and business strategists, emphasizing the need for robust technical infrastructure, engaging and diverse content, seamless user access, and transparent and fair economic systems. By addressing these key areas, metaverse platforms can improve user satisfaction, build a loyal user base, and secure long-term success in an increasingly competitive market.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• The Journal of Information Systems
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• v.32 no.3
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• pp.19-39
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• 2023

Purpose This study aims to identify user experience factors that can enhance both metaverse utilization and satisfaction based on the honeycomb model. For this we presented two research questions: first, what are the experience factors of metaverse users? Second, do metaverse user experience factors impact satisfaction? Design/methodology/approach To address these questions, a mixed-methodology approach is employed, including text mining techniques to analyze online reviews and quantitative econometric analysis to reveal the relationship between user experience factors and satisfaction. A total of 69,880 reviews and ratings data were collected. Findings The analysis revealed eight metaverse user experience factors: entertainment, operability, virtual reality, immersion, economic activity, visual performance, avatar, and sociality, all of which were found to have a positive impact on 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 the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1422-1427
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• 2013

This paper proposed a movie retrieval system based on sentimental keywords extracted from user's movie reviews. At first, sentimental keyword dictionary is manually constructed by applying morphological analysis to user's movie reviews, and then keyword weights in the dictionary are calculated for each movie with TF-IDF. By using these results, the proposed system classify sentimental categories of movies and rank classified movies. Without reading any movie reviews, users can retrieve movies through queries composed by sentimental keywords.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.91-107
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• 2023

With the recent expansion of the commerce ecosystem, a large number of user evaluations have been produced. Accordingly, attempts to create business insights using user evaluation data have been actively made. However, since user evaluation can change after the user experiences the product, it is difficult to say that the analysis based only on reviews immediately after purchase fully reflects the user's evaluation of the product. Moreover, studies conducted so far on user evaluation have overlooked the fact that the length of time a user has used a product can affect the user's product evaluation. Therefore, in this study, we build a model that predicts the direction of change in the user's rating after use from the user's rating and reviews immediately after purchase. In particular, the proposed model reflects the product's period of use in predicting the change direction of the star rating. However, since the posterior information on the duration of product use cannot be used as input in the inference process, we propose a structure that utilizes information about the product's period of use using an auxiliary classifier. As a result of an experiment using 599,889 user evaluation data collected from the shopping platform 'N' company, we confirmed that the proposed model performed better than the existing model in terms of accuracy.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.418-426
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• 2015

Smartphone users select restaurants using restaurant recommendation apps and use previous visitors' reviews as key decision-making information. It has not been studied yet how users react to negative reviews and how their reactions lead into the dining decision. In this study, we examined whether there are differences in the influence of negative reviews on intention to visit the restaurant according to users' decision making styles. This study confirmed negative reviews affect user decision differently according to three attributes (food, service, and atmosphere), and also partially verified that the effects of negative reviews are different according to decision-making style.