• Korean Journal of Human Ecology
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• v.18 no.5
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• pp.1063-1075
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• 2009

This study intends to provide foundational data for enhancing the welfare of customers purchasing digital products through analyzing the notes from written on-line reviews. The data used for the analysis are 6,342 on-line reviews for cell phones and digital cameras released from November, 2007 until April, 2008, which was posted on Naver Knowledge Shopping from November, 2007 until June, 2008. Through the on-line reviews, this article analyzed the evaluations on the digital products' hardware, software, design, service, price, and other criteria and the customers' emotional experience in the process of purchase, use, and possession. According to the results of the analysis, negative evaluation and emotional experience were originated from the company's information provision methods and purchase process. In addition, insufficient information searches in the process of online purchases, consumers' low right consciousness, and impolite on-line reviews were also problematic. Customers' evaluations and emotional experiences on digital products were conducted in a complex way. Based on that, this research makes suggestions in the company's marketing, customer education, and theoretical aspect.

• Journal of Distribution Science
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• v.19 no.4
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• pp.25-35
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• 2021

Purpose: This study aims to analyze the factors affecting customer satisfaction in the customer reviews of omni-channel, posted on Internet blogs, cafes, and YouTube using text mining analysis. Research, data, and Methodology: In this study, frequency analysis is performed and the LDA (Latent Dirichlet Allocation) is used to analyze social big data to respond to reviewers' reaction to the recently opened omni-channel shopping reviews by L Shopping Company. Additionally, based on the topic analysis, we conduct a sentiment analysis on purchase reviews and analyze the characteristics of each topic on the positive or negative sentiments of omni-channel app users. Results: As a result of a topic analysis, four main topics are derived: delivery and events, economic value, recommendations and convenience, and product quality and brand awareness. The emotional analysis reveals that the reviewers have many positive evaluations for price policy and product promotion, but negative evaluations for app use, delivery, and product quality. Conclusions: Retailers can establish customized marketing strategies by identifying the customer's major interests through text mining analysis. Additionally, the analysis of sentiment by subject becomes an important indicator for developing products and services that customers want by identifying areas that satisfy customers and areas that evoke negative reactions.

• 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 Processing Systems
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• v.16 no.3
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• pp.718-732
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• 2020

Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.

• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.67-74
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• 2023

Purpose: This study aims to investigate how the online review usefulness affect consumers' shopping behavior in non-face-to-face shopping, which is now very common format of shopping environment after COVID-19 pandemic. Factors influencing online reviews were determined as quantity of review, agreement of review and characteristic of review based on research by existing researchers. Research design, data, and methodology: Customers in their teens to 60s who had experience of checking online reviews and purchasing products were surveyed using a Google questionnaire form from January 15, 2022 to February 19, 2022. To verify the validity and reliability of the research model, confirmatory factor analysis and discriminant validity analysis were conducted. In addition, the causal relationship between factors was verified through path analysis. Results: As a result, quantity of review and agreement of review had a statistically significant effect on review usefulness. However, characteristic of review did not have a statistically significant influence on review usefulness. And review usefulness had a statistically significant effect on attitude and purchase intention. Conclusions: This study investigated the factors affecting usefulness of online reviews and empirically analyzed the effects of online reviews on consumer attitudes and purchase intentions providing practical and theoretical implications for corporate online review management.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.169-184
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• 2016

Smartphone use is rapidly spreading due to the advantage of being able to connect to the Internet anytime, anywhere--and mobile app development is developing accordingly. The characteristic of the mobile app market is the ability to launch one's app into foreign markets with ease as long as the platform is the same. However, a large amount of prior research asserts that consumers behave differently depending on their culture and, from this perspective, various studies comparing the differences between consumer behaviors in different countries exist. Accordingly, this research, which uses online product reviews (OPRs) in order to analyze the cultural differences in consumer behavior comparatively by nationality, proposes to compare the U.S. and South Korea by selecting ten apps which were released in both countries in order to perform a sentimental analysis on the basis of star ratings and, based on those ratings, to interpret the sentiments in reviews. This research was carried out to determine whether, on the basis of ratings analysis, analysis of review contents for sentiment differences, analysis of LDA topic modeling, and co-occurrence analysis, actual differences in online reviews in South Korea and the U.S. exist due to cultural differences. The results confirm that the sentiments of reviews for both countries appear to be more negative than those of star ratings. Furthermore, while no great differences in high-raking review topics between the U.S. and South Korea were revealed through topic modeling and co-occurrence analyses, numerous differences in sentiment appeared-confirming that Koreans evaluated the mobile apps' specialized functions, while Americans evaluated the mobile apps in their entirety. This research reveals that differences in sentiments regarding mobile app reviews due to cultural differences between Koreans and Americans can be seen through sentiment analysis and topic modeling, and, through co-occurrence analysis, that they were able to examine trends in review-writing for each country.

• Korean Journal of Child Studies
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• v.26 no.1
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• pp.203-216
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• 2005

Many picture books are published every year. Book reviews can play an important role in building knowledge about newly published book. This study analyzed data the coverage and content of reviews in journals with a view to helping librarians and parents become more aware of content and coverage of reviews for picture books. Variations of bibliographic and ordering information appeared among all journals. Most reviews typically included a plot summary and a general statement about the illustrations. Overall, journals provided more comments on literary elements than artistic elements. However, reviews provided insufficient information about the background of reviewers. Physical description of the books appeared in 8.81 % of the sample.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.259-266
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• 2020

In this paper, we propose a novel scheme for product effect analysis, termed PEM, to find out the effectiveness of products used for improving the current condition, such as health supplements and cosmetics, by utilizing online customer reviews. The proposed technique preprocesses online customer reviews to remove advertisements automatically, constructs the word dictionary composed of symptoms, effects, increases, and decreases, and measures products' effects from online customer reviews. Using Naver Shopping Review datasets collected through crawling, we evaluated the performance of PEM compared to those of two methods using traditional sentiment dictionary and an RNN model, respectively. Our experimental results shows that the proposed technique outperforms the other two methods. In addition, by applying the proposed technique to the online customer reviews of atopic dermatitis and acne, effective treatments for them were found appeared on online social media. The proposed product effect analysis technique presented in this paper can be applied to various products and social media because it can score the effect of products from reviews of various media including blogs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6097-6120
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• 2019

Online consumer reviews (OCRs) provide product information and recommendations especially pictures in reviews depict the true information about the product. This study investigates the influence of pictured reviews on online seller (for a particular product of a seller) rating with moderating effect of price, brand type (foreign vs local), goods type (experience vs search), and brand familiarity. Multiple robust linear regression analysis with moderation interaction and quadratic effect used to explain the relationship of the explanatory variables with the criterion variable. We collected cross-sectional data from the two most renowned Chinese online shopping platforms (B2C) of total 15,621 product links. Results show that higher number of reviews with a low ratio of picture reviews response negative effect on rating, whereas the lower number of reviews with a high ratio of picture reviews response positive effect on the rating. In overall picture in the reviews improve the online seller product rating. For the moderation effect, results show that price and brand familiarity have a positive interaction effect on the relation of pictured reviews and rating whereas experience goods have less negative effect comparing search goods. Finally, local brand has less negative interaction effect comparing foreign brand to pictured reviews and rating.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.465-472
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• 2021

In this paper, we present an approach for detection of adverse drug reactions from drug reviews to compensate limitations of the spontaneous adverse drug reactions reporting system. Considering negative reviews usually contain adverse drug reactions, sentiment analysis on drug reviews was performed and extracted negative reviews. After then, MedDRA dictionary and named entity recognition were applied to the negative reviews to detect adverse drug reactions. For the experiment, drug reviews of Celecoxib, Naproxen, and Ibuprofen from 5 drug review sites, and analyzed. Our results showed that detection of adverse drug reactions is able to compensate to limitation of under-reporting in the spontaneous adverse drugs reactions reporting system.