• Information Systems Review
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• v.20 no.2
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• pp.163-188
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• 2018

When people try to find restaurant information via search engine results, they look at posts not only from sites with solely restaurant reviews but also from sites with restaurant unrelated contents. This study aims to investigate whether relevance between post and blog type affects users' trust toward a review. This study also attempts to check if the above effects interact with age. We designed a restaurant review post for two different blogs: one featuring restaurant review and another that does not feature restaurant reviews. After our participants visited one restaurant review post, they answered our questionnaire. We conducted an online survey on 206 participants to test our research model. Results show that 1) the effect of relevance between post and blog type on message credibility, which is users' trust toward restaurant reviews, is not greater when posts are consistent with the theme of a blog. 2) Among users who are over 30 years old, relevance between post and blog type moderates the relationship between media skepticism, which is users' feeling of mistrust toward blog, and belief in expertise, that is, users' belief that the review post provides sufficient restaurant information. 3) Users' perceived value of the restaurant review post mediates the relationship between users' belief in the expertise in a post and users' intention to seek additional information.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.80-91
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• 2017

The purpose of paper is to confirm the role of E-WOM(Electronic Worth of Mouth) in decision making of selecting the mobile application via smart-phone or tablet pc. Particularly i wished to confirm the effects of others' positive or negative reviews in purchasing(free downloading) mobile applications. To resolve these research questions, the secondary data or previous research were collected and arranged theoretically. From literature research, i made out the proposed model to explain the relationships between the variables, executed the operational definitions and 14 Hypotheses were established, collected the survey data of 228 mobile application users. Using the empirical test analysis, previous performances to confirm the construct validity and internal consistency and PLS(Partial Least Square) modelling method was executed. The test result showed that proposed relations of variables was empirically identified, therefore, i got the conclusion as followings; First, attributes of mobile application users' reviews have the effects positively to usefulness perception and expected performance. Second, it was significantly tested Usefulness of Online Review and Expected Performance. Second, Usefulness of Online Review, Source Credibility and Expected Performance have effect positively to Intention of Review Adoption.

• Knowledge Management Research
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• v.23 no.3
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• pp.193-212
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• 2022

Many studies have proposed several factors that affect review helpfulness. Previous studies have investigated the effect of quantitative factors (e.g., star ratings) and affective factors (e.g., sentiment scores) on review helpfulness. Online reviews contain titles and contents, but existing studies focus on the review content. However, there is a limitation to investigating the factors that affect review helpfulness based on the review content without considering the review title. However, previous studies independently investigated the effect of review content and title on review helpfulness. However, it may ignore the potential impact of similarity between review titles and content on review helpfulness. This study used text consistency between review titles and content affect review helpfulness based on the mere exposure effect theory. We also considered the role of information clearness, review length, and source reliability. The results show that text consistency between the review title and the content negatively affects the review helpfulness. Furthermore, we found that information clearness and source reliability weaken the negative effects of text consistency on review helpfulness.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.515-518
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• 2020

영상 콘텐츠 생산 간편화와 방송 채널 운영의 편리화에 따른 '영상의 시대'가 도래함에 따라 여러 제품에 대한 리뷰 영상이 관심을 받고 있다. 본 연구에서는 리뷰 영상의 언어와 비언어적 감성 분석을 토대로 통합 신뢰도 분석 시스템을 제안한다. 이를 위해, 영상 속 음성의 언어 감성 분석과 리뷰어의 표정 분석을 통해 얻은 각 감성값을 추출하고 정량화한다. 이후 표준화된 언어, 비언어적 감성 값에 대한 통합 신뢰도 분석을 진행한다. 결과적으로, 리뷰 영상에 대한 신뢰도를 객관화된 지표로써 평가할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.77-79
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• 2019

블록체인을 이용하여 리뷰의 무결성을 검증하고 리뷰 제공자에게 보상을 지급한다. 기존의 리뷰 시스템에서는 돈을 받고 광고해주거나 악의적인 목적을 가진 악성 리뷰가 많이 존재한다. 리뷰 제공자에 대한 적절한 보상이 없어 리뷰 제공자가 직접 광고 유치 등을 통해 수익을 창출해왔다. 이 리뷰 시스템을 통해 리뷰 제공자는 정당한 노력의 보상을 받을 수 있고 사용자들도 신뢰할 수 있는 정보를 제공 받을 수 있다. 이러한 시스템을 통해 선순환적인 리뷰 생태계를 구축하고자 한다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.153-160
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• 2021

With the advent of the "age of video" due to the simplification of video content production and the convenience of broadcasting channel operation, review videos on various products are drawing attention. We proposes RASIA, an integrated reliability analysis system based on verbal and nonverbal sentiment analysis of review videos. RASIA extracts and quantifies each emotional value obtained through language sentiment analysis and facial analysis of the reviewer in the video. Subsequently, we conduct an integrated reliability analysis of standardized verbal and nonverbal sentimental values. RASIA provide an new objective indicator to evaluate the reliability of the review video.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Information Management
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• v.43 no.1
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• pp.109-134
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• 2012

This study, focusing on process of online Word-of-Mouth(oWOM) adoption in applications market which is a major issue of recent mobile industry, tried to empirically analyze how main characteristics of oWOM affect trust and process of oWOM adoption. To do this, based on understanding about applications market and precedent studies on online communication and Elaboration Likelihood Model(ELM), I developed the research model and proposed seven hypotheses. The subjects were smart phone users who ever used review in mobile applications market. The study method was questionnaire survey. As a result, trust in review was suggested as prerequisite for consumers to accept on-line review in mobile applications market. And it was empirically proved that for the customers to feel trust, these are necessary - positive assessment on argument quality, vividness of delivered explanation, and neutrality of message. The theoretical implications of this study are that based on studies on oWOM, factors affecting trust in review were explored in the environment of mobile applications market with less judgement clues for decision making compared to other on-line media and then, these factors were conceptualized. From the practical view, this study suggested implication on what attributes companies or developers can strategically utilize while investigating prerequisites of oWOM adoption.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.35-56
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• 2023

With the development of information and communication technology, numerous reviews are continuously posted on websites, which causes information overload problems. Therefore, users face difficulty in exploring reviews for their decision-making. To solve such a problem, many studies on review helpfulness prediction have been actively conducted to provide users with helpful and reliable reviews. Existing studies predict review helpfulness mainly based on the features included in the review. However, such studies disable providing the reason why predicted reviews are helpful. Therefore, this study aims to propose a methodology for applying eXplainable Artificial Intelligence (XAI) techniques in review helpfulness prediction to address such a limitation. This study uses restaurant reviews collected from Yelp.com to compare the prediction performance of six models widely used in previous studies. Next, we propose an explainable review helpfulness prediction model by applying the XAI technique to the model with the best prediction performance. Therefore, the methodology proposed in this study can recommend helpful reviews in the user's purchasing decision-making process and provide the interpretation of why such predicted reviews are helpful.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.