Online Review Data Research Articles

Application of traditional culture in modern cultural and creative product design in the era of artificial intelligence

Abstract This paper obtains design demand keywords from online review data of cultural and creative products and builds a framework of modern cultural and creative product design demand in the era of artificial intelligence by combining with consumption intention. Oriented to integrating traditional culture in the design of cultural and creative products, the graphic image processing technology is used to extract conventional cultural elements and optimize the pattern and image enhancement of cultural and creative products. Then the parametric technology is used to transform the design of traditional cultural symbols and elements. Setting satisfaction scores from five aspects: color, shape, material, function and cultural connotation, using satisfaction index to score the cultural and creative products of traditional culture, and carrying out the test of conventional cultural imagery conveyance of cultural and creative products, “artistic,” “full of representative significance The mean values of “artistic”, “representative” and “cultural value” are 4.573, 4.602, 4.518, and the P value is 0.000, which is significant, and the integration of the essential visual characteristics of the theme design of the cultural and creative products is effective. The works combined with the modern cultural and creative product design framework can highlight the traditional cultural qualities.

Research on the Perception and Dissemination of Tourist Attraction Images in Tourist Cities under Cross-Cultural Backgrounds: Data from International Tourists’ Online Reviews

This paper investigates the perception and dissemination strategies of tourist images in tourist cities from the perspective of international tourists with cross-cultural characteristics, coming from different countries. It selects 13 tourist cities in Jiangsu Province, China, as case studies. By analyzing online review data from international tourists entering the country, the study examines both positive and negative perceptions of tourist attraction images. The results reveal that the language and volume of the reviews reflect the origins and scale of the international tourists, while the content of these reviews indicates their preferences for tourist attractions. A comparative analysis of the review data suggests that there are differences in the perceived images of tourist cities. The conclusions of this study suggest that tourism city managers, tourism business operators, and local residents can make efforts to improve the city’s tourism development. These improvements can be achieved by utilizing international media platforms to disseminate tourism images, enhancing the quality of tourist attraction operations, and strengthening cultural exchanges in tourism.

Product ranking through fusing the wisdom of consumers extracted from online reviews on multiple platforms

Ranking products based on online reviews has become an important measure to support consumers’ purchasing decisions. How to make an effective decision considering online review information from different e-commerce platforms is a challenge. In this regard, this study introduces a large-scale group decision-making (LSGDM) method to assist users in making purchasing decisions by extracting the collective wisdom of reviewers across different platforms. First, we use the lexical analysis system, term frequency-inverse document frequency algorithm, and sentiment dictionary to process online review data and obtain product attributes, the weights of attributes, and sentiment scores of reviews, respectively. The weights of platforms are determined using an integrated method that considers the characteristics of each platform. Afterwards, we present an LSGDM method to achieve the coordination of the wisdom of reviewers. We collect online reviews of four mobile phones from Tmall, JD and Suning, providing experimental analyses to demonstrate the applicability of our proposed method.

Reexamining review variance and movie sales: the inverted-U-shaped relationship and boundary conditions

PurposeThis study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect.Design/methodology/approachBased on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors that moderate these relationships. Review data from websites targeting the film industry in the USA and South Korea (Korea) were collected to empirically analyze the authors' hypothesis, and panel regression analysis was used for confirmation.FindingsModerated by interactive and motivational factors, review variance exhibits an inverse-U-shaped relationship with review variance. Specifically, as an interaction factor, review valence and owned social media (OSM) resulted in positive interaction effects, and as a motivation factor, the number of alternatives exhibited a positive interaction effect with review variance. The effect of review variance was less pronounced in the USA than in Korea.Originality/valueThe study outcomes reveal a nonlinear relationship between review variance and sales, thus supporting the contradictory findings of previous studies. This study contributes to the literature by using the HSM as a theoretical framework to verify various HSM mechanisms using online review data. This exploratory study also contributes to the international marketing literature by showing that the effects of review variance vary across cultures.

Imbalanced sentiment classification of online reviews based on SimBERT

The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods.

Impacts of user-generated images in online reviews on customer engagement: A panel data analysis

Visual content has become an integral component of customers’ experience sharing, with customers increasingly searching for visual content in online reviews prior to making purchases. This study examines the effects of customer-generated images in online reviews on subsequent customer engagement using a multimethod design combining computer vision technique and panel data analysis. Based on online review data for 300 restaurants, findings revealed the following: 1) the ratio of pictorial reviews positively influenced subsequent review volume and average review length, whereas the effect on subsequent review valence was not significant; 2) review text–photo sentiment disparity had a complex impact on customer engagement (i.e., an inverse U-shaped relationship with subsequent review volume and a positive and negative linear relationship with subsequent average review length and review valence, respectively); and 3) business price level could mitigate the above effects. This study contributes to the literature on electronic word of mouth and customer engagement.

Tourism forecasting with granular sentiment analysis

Generic sentiment calculations cannot fully reflect tourists' preferences, whereas fine-grained sentiment analysis identifies tourists' precise attitudes. This study forecasted visitor arrivals at two tourist attractions in China using Internet data from multiple sources. Empirical results indicate that 1) fine-grained sentiment analysis of online review data can substantially improve tourism demand models' forecasting performance; 2) combining multidimensional sentiment analysis–based online review data with search engine data outperforms search engine data in tourism demand prediction; and 3) fine-grained sentiment analysis–based online review data and search engine data maintain stable predictive power during times of uncertainty.

다이내믹 토픽 모델링을 활용한 아프리카 사파리 관광 동향 분석

Tanzania in Africa is generating socio-economic benefits through safari tourism. Given that safari tourism centers on ecological resources and wildlife, tourists may feel less certain about what they will actually see or encounter. Tourists tend to utilize online reviews to reduce uncertainty and gain insights into trends. This study aims to understand the key topics and trend changes in perceptions of Tanzanian safari tourism products. English online review data from Safaribookings were crawled to accomplish the research purpose, and the Dynamic Topic Modeling (DTM) algorithm, which is suitable for temporal trend analysis, was utilized to address the research questions. The analysis revealed the following six major topics, all demonstrating trend changes: safari tourism recommendations, wildlife tourism experiences, experiences in transport payments, hotel and airline booking experiences, Mount Kilimanjaro tourism experiences, and tourism experiences in Zanzibar and Selous. The study results support that the DTM, which is used to overcome temporal limitations and implement systematic processes, is a systematic research method and can help expand the scope of research methodologies. Further, based on the analysis results, this study can aid stakeholders, such as safari marketers, in establishing Tanzania safari tourism marketing strategies.

Extracting product design guidance from online reviews: An explainable neural network-based approach

With the development of data mining techniques, user-generated data has become a valuable resource in diverse research areas. In product design research, many studies have been utilizing user data to discover implications for new product design. However, previous works focused on analyzing existing features, whereas companies also need strategies for new features. Some studies discovered new feature ideas from user data but did not provide design implications. This paper addresses the above limitation by extracting comprehensive design implications for both features from user data. The method first defines the lists of existing/new features and collects spec data for these features. Then, it constructs customer choice sets based on the online review and spec data. Regarding spec values, this study presents a newness merit function that reflects the changing value of new features and applies it to the choice sets. The final stage trains a neural network model based on choice sets and conducts SHAP (SHapley Additive exPlanations) on the model. The method draws design implications by further analyzing the resultant SHAP values. The suggested methodology was tested on real-world datasets. The result provides design guidance, including strategies for new features and recommended spec ranges for existing features. This article validates the result by showing that the obtained design implications are consistent with previous market research for product features.

An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data

We investigate a data-driven single-period inventory management problem with uncertain demand, where large amounts of textual online reviews and historical data are accessible. Unlike two-step frameworks (i.e., predict-then-optimization), we propose an end-to-end (E2E) framework that directly suggests the order quantity by leveraging a deep learning model that inputs textual online reviews and other demand-related feature data, without any intermediate steps such as text sentiment analysis. The E2E model does not require any prior assumptions about the demand distribution and can automatically determine the order quantity that minimizes the newsvendor cost by employing the information from real-world data. Our experiments, using publicly available real-world data, demonstrate that our method can significantly reduce the sum of overage and underage costs, outperforming other data-driven models proposed in recent years. Specifically, the inclusion of textual online review data improves ordering decisions by a 28.7% cost reduction.

Market segmentation based on customer experience dimensions extracted from online reviews using data mining

PurposeThis study aims to develop a practical and effective approach for market segmentation using customer experience dimensions derived from online reviews.Design/methodology/approachThe research investigates over 6,500 customer evaluations of food establishments on Taiwan’s Yelp platform through the Latent Dirichlet allocation (LDA) data mining approach. By using the LDA-derived experience dimensions, cluster analysis discloses market segments. Subsequently, sentiment analysis is used to scrutinize the emotional scores of each segment.FindingsMining online review data helps discern divergent and new customer experience dimensions and sheds light on the divergent preferences among identified customer segments concerning these dimensions. Moreover, the polarity of sentiments expressed by consumers varies across such segments.Research limitations/implicationsAnalyzing customer attributes extracted from online reviews for segmentation can enhance comprehension of customers’ needs. Further, using sentiment analysis and attributes of online reviews result in rich profiling of the identified segments, revealing gaps and opportunities for marketers.Originality/valueThis research presents a new approach to segmentation, which surmounts the restrictions of segmentation methods dependent on survey-based information. It contributes to the field and provides a valuable means for conducting customer-focused market segmentation. Furthermore, the suggested methodology is transferable across different sectors and not reliant on particular data sources, creating possibilities in diverse scenarios.

What online review features really matter? An explainable deep learning approach for hotel demand forecasting

AbstractAccurate demand forecasting plays a critical role in hotel revenue management. Online reviews have emerged as a viable information source for hotel demand forecasting. However, existing hotel demand forecasting studies leverage only sentiment information from online reviews, leading to capturing insufficient information. Furthermore, prevailing hotel demand forecasting methods either lack explainability or fail to capture local correlations within sequences. In this study, we (1) propose a comprehensive framework consisting of four components: expertise, sentiment, popularity, and novelty (ESPN framework), to investigate the impact of online reviews on hotel demand forecasting; (2) propose a novel dual attention‐based long short‐term memory convolutional neural network (DA‐LSTM‐CNN) model to optimize the model effectiveness. We collected online review data from Ctrip.com to evaluate our proposed ESPN framework and DA‐LSTM‐CNN model. The empirical results show that incorporating features derived from the ESPN improves forecasting accuracy and our DA‐LSTM‐CNN significantly outperforms the state‐of‐the‐art models. Further, we use a case study to illustrate the explainability of the DA‐LSTM‐CNN, which could guide future setups for hotel demand forecasting systems. We discuss how stakeholders can benefit from our proposed ESPN framework and DA‐LSTM‐CNN model.

Deep learning for comment spam and scams

As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.

Mengurai Pengalaman Positif Tamu Saat Menginap di Sheraton Senggigi Beach Resort

Customer reviews on the internet play an essential role in generating opinions in electronic Word-of-Mouth (eWOM); This review serves as an online communication tool that significantly influences the perception of the following potential user. The purpose of this study is to understand the opinions that often appear in actual customer reviews that give an Excellent and Very Good rating. This study collected 1.515 online review data from the Tripadvisor website using automated web data mining techniques. Next, we used the JMP Pro 14 software to parse and visualize the research data at the analysis stage. The data shows that the most critical customer satisfaction lies in non-physical services, namely "staff" services, where most of the reviews express the meaning of being very satisfied with the services provided by the staff. Meanwhile, in the aspect of physical attributes, there is an important note, namely the "room" aspect, where this term is used to refer to complaints of bedrooms and bathrooms that are old and dirty enough to require renewal. Physical attributes, such as pool, breakfast, beach, and restaurant, are satisfactory because they can meet customer expectations

Examining the negative relationship between length of stay at a hotel and customer satisfaction: evidence from online customer reviews

PurposeStudies that investigate the length of stay as a predictor of consumer post-purchase behavior are rare despite its importance in efficient hotel management. By analyzing online customer reviews, this study aims to fill this gap in the extant literature on the relationship between length of stay and customer satisfaction level.Design/methodology/approachThe authors collected and used online review data on hotels in London for this study. A series of linear regression analyses were conducted to examine the effect of length of stay on customer satisfaction as measured by review ratings. The authors used the Mahalanobis matching approach to confirm the empirical findings.FindingsThis analysis shows that length of stay is negatively associated with customer satisfaction. Additionally, the authors find that this negative relationship is stronger in high-end hotels than in low-end hotels.Research limitations/implicationsThe research findings contribute to the literature by shedding light on a new stream of research, namely, length of stay. Additionally, the research findings offer novel insights that could help hotel managers understand the trade-off between longer stays and customer satisfaction.Originality/valueTo the best of the authors’ knowledge, this is one of the first few studies to show the systematic impact of length of stay on the valence of online review ratings, as well as the moderating effect of hotel levels by analyzing customer online reviews on hotel experiences.

온라인 리뷰 데이터 기반의조직몰입도 측정 체계 구축에 대한 연구

온라인 리뷰 데이터 기반의조직몰입도 측정 체계 구축에 대한 연구

Towards a Review-Analytics-as-a-Service (RAaaS) Framework for SMEs: A Case Study on Review Fraud Detection and Understanding

With the advancement of internet technology, customers increasingly rely on online reviews as a valuable source of information. The study aims to develop a marketing data analytics framework to manage online reviews, especially fake reviews, which have become a significant issue undermining the creditability of online review systems. As small and medium-sized enterprises often lack the capabilities to automatically derive customer insights from online reviews, this study proposes a cost-effective, extensible Review-Analytics-as-a-Service (RAaaS) framework that can be operated by non-data specialists to facilitate online review data analytics. We demonstrate the framework’s application by using two datasets with more than 400,000 online reviews from Yelp to simulate live platforms and demonstrate an analytic flow of review fraud detection and understanding. The findings reveal insights into the influence of fake reviews on product ranking and exposure rate. Moreover, it was found that there was a higher concentration of sadness and anger in fake reviews (vs. organic reviews). In addition, fake reviews tend to be shorter, more extreme (with the use of strong adverbs), and have different patterns of topic distribution. This study has important implications for different stakeholder groups including, but not limited to, SMEs, review platforms and customers.

Weak tagging and imbalanced networks for online review sentiment classification

Sentiment classification aims to complete the automatic judgment task of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning models require a large number of manually annotated samples of sentiment tendency for supervised training. Faced with massive online review data, the feasibility of manual tagging is worrisome. In addition, the traditional deep learning model ignores the imbalanced distribution of the number of classification samples, which will lead to a decline in classification performance in the practical application of the model. Considering that the online review data contains weak tagging information such as scores and labels, and the distribution is imbalanced, a weak tagging and imbalanced networks for online review sentiment classification is constructed. The experimental results show that the model significantly outperforms the traditional deep learning model in the sentiment classification task of hotel review data.

Artificial intelligence in service industries: customers’ assessment of service production and resilient service operations

Artificial intelligence (AI) is increasingly embedded into service firms’ operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations.

Documenting the Rise of Anxiety in the United States across Space and Time by Using Text Analysis of Online Review Data

In this article, the author demonstrates how one can use large-scale and publicly available online review data to study the rise in anxiety in the United States. Using the anxiety keyword list from the dictionary compiled by Linguistic Inquiry and Word Count, the author analyzed the text of approximately 7 million online reviews submitted by Yelp reviewers across 13 U.S. states from 2006 to 2021. The overall pattern confirms existing discourse that anxiety has been constantly rising in Western societies since 2000. Beyond documenting the overall pattern, online review data enable the disaggregation of this pattern by geographies, price levels, and individuals, thereby providing a more comprehensive and detailed picture than previously documented in existing literature. Additional analysis shows that anxiety is increasing faster than other emotions, such as anger and sadness.