• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.43-61
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• 2016

Customer reviews help potential customers make purchasing decisions. However, the prevalence of reviews on websites push the customer to sift through them and change the focus from a mere search to identifying which of the available reviews are valuable and useful for the purchasing decision at hand. To identify useful reviews, websites have developed different mechanisms to give customers options when evaluating existing reviews. Websites allow users to rate the usefulness of a customer review as helpful or not. Amazon.com uses a ratio-type helpfulness, while Yelp.com uses a count-type usefulness index. This usefulness index provides helpful reviews to future potential purchasers. This study investigated the effects of sentiment and readability on useful votes for customer reviews. Similar studies on the relationship between sentiment and readability have focused on the ratio-type usefulness index utilized by websites such as Amazon.com. In this study, Yelp.com's count-type usefulness index for restaurant reviews was used to investigate the relationship between sentiment/readability and usefulness votes. Yelp.com's online customer reviews for stores in the beverage and food categories were used for the analysis. In total, 170,294 reviews containing information on a store's reputation and popularity were used. The control variables were the review length, store reputation, and popularity; the independent variables were the sentiment and readability, while the dependent variable was the number of helpful votes. The review rating is the moderating variable for the review sentiment and readability. The length is the number of characters in a review. The popularity is the number of reviews for a store, and the reputation is the general average rating of all reviews for a store. The readability of a review was calculated with the Coleman-Liau index. The sentiment is a positivity score for the review as calculated by SentiWordNet. The review rating is a preference score selected from 1 to 5 (stars) by the review author. The dependent variable (i.e., usefulness votes) used in this study is a count variable. Therefore, the Poisson regression model, which is commonly used to account for the discrete and nonnegative nature of count data, was applied in the analyses. The increase in helpful votes was assumed to follow a Poisson distribution. Because the Poisson model assumes an equal mean and variance and the data were over-dispersed, a negative binomial distribution model that allows for over-dispersion of the count variable was used for the estimation. Zero-inflated negative binomial regression was used to model count variables with excessive zeros and over-dispersed count outcome variables. With this model, the excess zeros were assumed to be generated through a separate process from the count values and therefore should be modeled as independently as possible. The results showed that positive sentiment had a negative effect on gaining useful votes for positive reviews but no significant effect on negative reviews. Poor readability had a negative effect on gaining useful votes and was not moderated by the review star ratings. These findings yield considerable managerial implications. The results are helpful for online websites when analyzing their review guidelines and identifying useful reviews for their business. Based on this study, positive reviews are not necessarily helpful; therefore, restaurants should consider which type of positive review is helpful for their business. Second, this study is beneficial for businesses and website designers in creating review mechanisms to know which type of reviews to highlight on their websites and which type of reviews can be beneficial to the business. Moreover, this study highlights the review systems employed by websites to allow their customers to post rating reviews.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.431-438
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• 2012

Recently, many mobile applications have been developed and published by increasing popularity of smart phone's market. However, a lack of research work is an issue for mobile applications' favoritism to users. In this paper, we have determined the acceptance factors on the existing smart phone applications in order to fit into mobile user's requirements. In addition, we have defined the modification of acceptance factors which are the interesting element of mobile applications, code users reviews of top 20 paid applications among android market applications, and analyse on what element can attract users' interest of mobile applications. As a result, we propose a development method of mobile applications based on the analyzed interesting elements. Our contribution is to provide useful information to mobile application companies for helping them to develop and publish successful mobile applications.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.381-391
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• 2020

This research article investigates the way eWOM in social media influences the formation of destination image through development of trust and satisfaction for the potential tourist. The research involved administering an 18-point questionnaire taking online reviews, tourist involvement, and eWOM, destination image components of trust and satisfaction as variables. Data was collected from 554 individuals forming a cross-section of social media users and analyzed using multi-variate techniques (Reliability, CFA, and SEM). Results indicate a positive and significant relationship between all except online review and destination trust and satisfaction. Indirect and direct effects indicate that eWOM fully mediates the relationship between destination satisfaction and involvement and partially mediates the relationship between destination trust and involvement. In the case of online reviews, eWOM acts as a full mediator between destination trust and destination satisfaction for the future traveler using social media. The study proposes that components of image vary depending upon the degree of involvement, volume online reviews and eWOM generated also termed as 'virality' and these in turn influence the intention to revisit or recommend a destination. The study highlights its utility for National Tourist Organizations (NTOs) and online travel intermediaries to enhance destination marketing efforts.

• Journal of Korea Game Society
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• v.17 no.3
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• pp.33-44
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• 2017

With the development of the Internet environment, various types of online reviews are being generated and exchanged among consumers to share their opinions. In line with this trend, companies are making efforts to analyze online reviews and use the results in various business activities such as marketing, sales, and product development. However, research on online review in industry related to 'Video Game' which is representative experience goods has not been performed enough. Therefore, this study analyzed STEAM community review data using machine learning techniques. We analyzed the factors affecting the opinion of other users' game review. We also propose managerial implications to incease user loyalty and usability.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• 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 Asian Finance, Economics and Business
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• v.9 no.6
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• pp.287-295
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• 2022

With the dramatic increase in mobile usage, more and more businesses see the potential of m-commerce. This study focuses on a subcategory of m-commerce, a mobile shopping application. To understand the purchase intention via m-commerce applications, this study is aimed to identify the main factors that are related to the applications and explore the influence of these factors on consumers' mobile shopping intention. This study uses quantitative research methods and selects Vietnam as its case study. The survey responses of 450 Vietnamese mobile shoppers were analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicated that online reviews, e-service quality, and information quality are significant predictors of behavior intention, and perceived risk negatively influences consumer online purchase intention via the applications. The content enriches the combined research of detailed and possible models with quality dimensions and risk perception. Practitioners such as e-retailers and developers can enhance the quality of applications and determine strategies to reach potential users and maximize revenue. M-commerce providers should pay adequate attention to credible and influential online reviews since mobile shoppers heavily rely on reading reviews before buying a product.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.12
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• pp.1270-1284
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• 2012

This article examines how individual differences on the need for cognitive closure and demographics influence consumer perception and the usage of consumer reviews in online shopping. Data were randomly collected from 2,381 U.S. online consumer review users through an online survey. The findings from multiple regression analyses revealed the different effects of the need for cognitive closure dimensions (i.e., preference for order and structure, preference for predictability, discomfort with ambiguity, closed-mindedness, and decisiveness) and demographic characteristics on consumer attitudes, perceived online review influence, benefits, persuasiveness, and review usage behavior. Finally, practical implications and prospects for future research are discussed.

• 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.

• Asia pacific journal of information systems
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• v.29 no.2
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• pp.181-202
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• 2019

The purpose of this study is to explore the effect of different types of simultaneous presentation (i.e., reviewer information, textual and visual content, and similarity between textual-visual contents) on review usefulness and review enjoyment in online restaurant reviews (ORRs), as they are interrelated yet have rarely been examined together in previous research. By using Latent Dirichlet Allocation (LDA) topic modeling and state-of-the-art machine learning (ML) methodologies, we found that review readability in textual content and salient objects in images in visual content have a significant impact on both review usefulness and review enjoyment. Moreover, similarity between textual-visual contents was found to be a major factor in determining review usefulness but not review enjoyment. As for reviewer information, reputation, expertise, and location of residence, these were found to be significantly related to review enjoyment. This study contributes to the body of knowledge on ORRs and provides valuable implications for general users and managers in the hospitality and tourism industries.