Tourism Development Mode of Historical and Cultural Blocks from the Perspective of Urban Renewal Based on Online Review Data:A Case Study of Nanluoguxiang in Beijing

Under the background of urban renewal, this paper re-explores the tourism development modeof Nanluoguxiang historical and cultural block based on online review data, and puts forward correspondingdevelopment strategies. As a cultural label of a city, historical and cultural blocks should be updated first inorder to achieve sustainable development. By using multi-source big data review and qualitative researchmethods, the perception evaluation of tourists in Nanluoguxiang is obtained, and the shortcomings ofcurrent tourism development mode are analyzed. Furthermore, corresponding improvement strategies andsuggestions are put forward, in order to provide some effective ideas for the sustainable development ofhistorical and cultural blocks in the future.

Effects of Online English Movie Reviews on Improving Students' Writing

This study aims to study the effectiveness of online English movie reviews for improving university students’English writing in China.English movie reviews can be profitably undertaken to improve university students’writing ability by reading English movie reviews online,discussing topics related to English movie reviews and writing English movie reviews collaboratively.University students have easy access to English movie reviews massively available on the Internet,which renders it possible and feasible for English teachers to use them to improve students’English writing.Online English movie reviews provide students with enough input of model texts,hence they can acquire some appropriate expressions before writing within a short period of time.In addition,discussion on English movie reviews through Emails and QQ platform can activate students’critical thinking to stimulate their original ideas for English movie review writing.Writing English movie reviews collaboratively with the help of Internet can develop students’confidence in writing because of peer feedback and less pressure.

Aspect-Level Opinion Mining of Online Customer Reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

Research on Online Reviews Impact on Commodity Sales in B2C E-commerce

The objective of this paper was to analyze the impact of online reviews on sales. Based on dual path model of commodity sales, an online reviews impact on the relationship between various factors, and then the theoretical hypothesis of each factor has been put forward in the model. As it is intuitive and strongly supported empirically, data including Chinese texts captured from Tmall.com was utilized, and then analyzed by SPSS and ROST CM6. Our empirical study on the reviews of Tmall.com indicated that the hypotheses are verified.

Research on Online Reviews Impact on Commodity Sales in B2C E-commerce

The objective of this paper was to analyze the impact of online reviews on sales. Based on dual path model of commodity sales, an online reviews impact on the relationship between various factors, and then the theoretical hypothesis of each factor has been put forward in the model. As it is intuitive and strongly supported empirically, data including Chinese texts captured from Tmall. com was utilized, and then analyzed by SPSS and ROST CM6. Our empirical study on the reviews of TmalLcom indicated that the hypotheses are verified.

Teacher feedback, peer comment and online review in L2 writing

It is apparent that writing has played an irreplaceable role in research and pedagogy because of the recognition of English as lingua franca and also the growing number of students going abroad for higher education.In the context of China,various kinds of feedback can be considered as significant factors to enhance Chinese English learners'writing ability.

Firm review revelation policy considering social ties among consumers

An optimization model is proposed to analyze the optimal review revelation policies and consumer online social network management strategies of e-commerce firms.The results show that displaying friend reviews to consumers does not necessarily increase firms profits.Only when positive reviews account for a large proportion of all the reviews and when the cost of showing friend reviews is not high,can showing friend reviews be more profitable than not showing such information.The distribution of social ties among consumers can affect firms profits.Even in the case that showing friend reviews to consumers is more profitable,an increase in the proportion of strong ties is not necessarily beneficial to firms.Only when the proportion of positive reviews is large enough,can firms profits increase with the increase in the proportion of strong ties among consumers.Moreover,the degree of consumer distrust in the average quality rating can also affect firms strategies for managing consumer online social networks.As the degree of consumer distrust in the average quality rating rises,firms are more likely to obtain higher profits by taking measures to increase the proportion of strong ties among consumers on their websites.

Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning

Metaverse is one of the main technologies in the daily lives of several people,such as education,tour systems,and mobile application services.Particularly,the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere.To provide an improved service,it is important to analyze online reviews that contain user satisfaction.Several previous studies have utilized traditional methods,such as the structural equation model(SEM)and technology acceptance method(TAM)for exploring user satisfaction,using limited survey data.These methods may not be appropriate for analyzing the users of mobile applications.To overcome this limitation,several researchers perform user experience analysis through online reviews and star ratings.However,some online reviews occasionally have inconsistencies between the star rating and the sentiment of the text.This variation disturbs the performance of machine learning.To alleviate the inconsistencies,Valence Aware Dictionary and sEntiment Reasoner(VADER),which is a sentiment classifier based on lexicon,is introduced.The current study aims to build a more accurate sentiment classifier based on machine learning with VADER.In this study,five sentiment classifiers are used,such as Naïve Bayes,K-Nearest Neighbors(KNN),Logistic Regression,Light Gradient Boosting Machine(LightGBM),and Categorical boosting algorithm(Catboost)with three embedding methods(Bag-of-Words(BoW),Term Frequency-Inverse Document Frequency(TF-IDF),Word2Vec).The results show that classifiers that apply VADER outperform those that do not apply VADER,excluding one classifier(Logistic Regression with Word2Vec).Moreover,LightGBM with TF-IDF has the highest accuracy 88.68%among other models.

A Structural Topic Model for Exploring User Satisfaction with Mobile Payments

This study explored user satisfaction with mobile payments by applying a novel structural topic model.Specifically,we collected 17,927 online reviews of a specific mobile payment(i.e.,PayPal).Then,we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment.Consequently,we discovered that“lack of reliability”and“poor customer service”tend to appear in negative reviews.Whereas,the terms“convenience,”“user-friendly interface,”“simple process,”and“secure system”tend to appear in positive reviews.On the basis of information system success theory,we categorized the topics“convenience,”“user-friendly interface,”and“simple process,”as system quality.In addition,“poor customer service”was categorized as service quality.Furthermore,based on the previous studies of trust and security,“lack of reliability”and“secure system”were categorized as trust and security,respectively.These outcomes indicate that users are satisfied when they perceive that system quality and security of specific mobile payments are great.On the contrary,users are dissatisfied when they feel that service quality and reliability of specific mobile payments is lacking.Overall,our research implies that a novel structural topic model is an effective method to explore mobile payment user experience.

Empirical Research on Brand Spillover Effect of Agricultural Product under the Background of Product Injury Crisis

By referring to attribution theory,influence factor and internal mechanism of crisis brand generating two kinds of spillover effects are explored. In this paper,the influence mechanism on spillover effect is explored from the angles of network and industry information. Results show that high online review dispersion is easy to cause that consumer attributes the responsibility to the outside of the enterprise,while low online review dispersion is easy to cause that consumer attributes the responsibility to the inside of the enterprise; high industry base rate information is easy to cause that consumer attributes the responsibility to the outside of the enterprise,while low industry base rate information is easy to cause that consumer attributes the responsibility to the inside of the enterprise. When consumer attributes the responsibility to the outside of the enterprise,infectious network spillover is easy to occur. When consumer attributes the responsibility to the inside of the enterprise,contrast network spillover is easy to occur. Consumer's existing brand knowledge regulates the influence of online review dispersion on responsibility attribution and the influence of industry base rate information on responsibility attribution. Research result could provide effective theoretic reference and management suggestion for crisis enterprise and even whole industry responding to product injury crisis.

Pricing Decision of E-Commerce Supply Chains with Return and Online Review of Product Quality

The online review function helps consumers grasp more real product information and reduce the possibility of returning, but it may also damage firms’ reputations or profits. However, few studies considered the relationship between online reviews and consumer returns. Based on this, we develop an e-commerce supply chain(E-SC) game model consisting of a single manufacturer and a single e-platform, aiming to explore the relationship between consumer returns and online reviews and to analyze the impact on both the decision-making of E-SC members and their profits. We find that there is a negative relationship between consumer returns and online reviews of product quality, and consumer returns make the pricing decisions in the two scenarios of yes/no online reviews move toward two different directions. Only when the online review is positive and higher than a certain threshold will it have a positive impact on sales and E-SC members’ profits. Finally, we design a new “commission joint returns and quality improvement costs sharing” contract to optimize the decentralized model with online reviews, and we find that the higher the accuracy of product information, the less conducive the contract applied to E-SC.

PRODUCTS RANKING THROUGH ASPECT-BASED SENTIMENT ANALYSIS OF ONLINE HETEROGENEOUS REVIEWS

With the rapid growth of online shopping platforms, more and more customers intend to share theirshopping experience and product reviews on the Internet. Both large quantity and various forms ofonline reviews bring difficulties for potential consumers to summary all the heterogenous reviews forreference. This paper proposes a new ranking method through online reviews based on differentaspects of the alternative products, which combines both objective and subjective sentiment values.Firstly, weights of these aspects are determined with LDA topic model to calculate the objectivesentiment value of the product. During this process, the realistic meaning of each aspect is alsosummarized. Then, consumers＇ personalized preferences are taken into consideration while calculatingtotal scores of alternative products. Meanwhile, comparative superiority between every two productsalso contributes to their final scores. Therefore, a directed graph model is constructed and the finalscore of each product is computed by improved PageRank algorithm. Finally, a case study is given toillustrate the feasibility and effectiveness of the proposed method. The result demonstrates that whileconsidering only objective sentiment values of the product, the ranking result obtained by our proposedmethod has a strong correlation with the actual sales orders. On the other hand, if consumers expresssubjective preferences towards a certain aspect, the final ranking is also consistent with the actualperformance of alternative products. It provides a new research idea for online customer review miningand personalized recommendation.

PRICING STRATEGIES IN A SUPPLY CHAIN WITH MULTI-MANUFACTURER AND A COMMON RETAILER UNDER ONLINE REVIEWS

Online reviews play an important role in consumer purchasing behavior when shopping online, and in turn affect pricing strategies of sellers. We consider a supply chain consisting of three competitive manufacturers and an e-tailer, which sells multiple substitutable products procured from different manufacturers. Considering reviews in terms of quality information and fit information, we study the effect of online reviews on consumers＇ purchasing decisions as well as pricing strategies of manufacturers and the e-retailer. Through modelling customer choice with online reviews based on neo-Hoteling model and solving Stackelberg game composed by manufacturers and e-tailer, we obtain the optimal equilibrium prices of manufacturers and the e-tailer. And we distinguish two kinds of effect from online reviews： effect of the opinion bias in product quality and effect of the match informativeness. Furthermore, compared to the case without online reviews, we find online reviews improve a manufacturer＇s optimal wholesale price, profit as well as the retailer＇s optimal retailing price, when the adjusted quality revealed by reviews is high; however, online reviews can improve the e-tailer＇s optimal profit, only when the standard deviation of products＇ quality revealed by reviews is large enough. Last, we get that the increase of competing manufacturers＇ quantity would change the dominance between two kinds of effect from reviews on supply chain members.

TOWARDS EXPLORING WHEN AND WHAT PEOPLE REVIEWED FOR THEIR ONLINE SHOPPING EXPERIENCES

Web 2.0 technologies have attracted an increasing number of people with various backgrounds to become active online writers and viewers. As a result, exploring reviewers＇ opinions from a huge number of online reviews has become more important and simultaneously more difficult than ever before. In this paper, we first present a methodological framework to study the ＂purchasing-reviewing＂ behavior dynamics of online customers. Then, we propose a review-to-aspect mapping method to explore reviewers＇ opinions from the massive and sparse online reviews. The analytical and experimental results with real data demonstrate that online customers can be sectioned into groups in accordance with their reviewing behaviors and that people within the same group may have similar reviewing motivations and concerns for an online shopping experience.

TOOR： A Novel Product Title Optimization Method Based on Online Reviews in E-commerce

Titles of online products play an important role in attracting consumers and promoting product sales in e-commerce. However, current online product titles only cover basic features and cannot reflect the preferences of consumers exactly. To address this problem, this research proposed an online title optimization method based on the analysis of online reviews, which is called TOOR （Title Optimization based on Online Reviews）. In this research, we analyzed and compared product features extracted from online product titles and online reviews from the point of view of consumers and applied features extracted from reviews to title optimization. In order to verify the effectiveness of the proposed method, two experiments were conducted in this paper, selecting four typical smartphones as experiment samples and Taobao.com as the data resources. The experimental results indicated that features extracted from online reviews can better reflect the consumers＇ concern, and the tires optimized by the TOOR method are more appealing to consumers and have higher click-through rates.

Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

Do you respond sincerely? How sellers'responses to online reviews affect customer relationship and repurchase intention

For many online businesses,online reviews are crucially important to managing reputation,word-of-mouth sales,and,ultimately,their survival.Hence,more and more on line business owners are posting public responses to both positive and negative reviews.But do these responses change anything?And,if so,which types of responses are the most effective for strengthening customer relationship and increasing the chances of repeat business?With two surveys and a series of partial least squares-structural equation modeling(PLS-SEM)analyses,we tested three releva nt hypotheses to an swer these questions.The results show that when busi ness replies to online reviews with promotional information,consumers perceive the seller to be self-interested,and both relati on ship quality and repurchase intenti on decrease.However,sincere responses that do not contain promotional information,such as gratitude and apology,are highly correlated to positive relationship quality and the likelihood of future repeat business.These findings enrich the academic literature on on line reviews,and the recommendations stemming from our results should be of interest to any business that relies on their online reputation for survival.

Exploring the characteristics of tourism industry by analyzing consumer review contents from social media:a case study of Bamako,Mali

In this Web 2.0 era,various and massive tourist experiences and reviews presented on social networks have become important information for tourism research.In this paper,we apply social media to explore and study the tourism industry of Bamako,Mali.Over 2000 reviewers and their comments about Bamako’s hotels and restaurants from TripAdvisor and Facebook were collected.Also,we integrate official tourism statistic data and field surveying data into the online review dataset.Data mining and statistic method are used to analyze the data for purpose of exploring the characteristics about tourism industry in Bamako.And we find that:(i)Most tourists are coming to Bamako for business purpose,and they incline to choose the hotels with better service and security condition;(ii)Comments on social media would greatly affect travelers’choice on hotels;(iii)Most travelers are satisfied about Bamako’s accommodation services.

Research on COVID-19 Internet Derived Public Opinions Prediction Based on the Event Evolution Graph

As one of the ways to reflect the views of the masses in modern society,online reviews have great value in public opinion research.The analysis of potential public opinion information from online reviews has a certain value for the government to clarify the next work direction.In this paper,the event evolution graph is designed to make COVID-19 network public opinion prediction.The causal relationship was extracted in the network reviews after the COVID-19 incident to build an event evolution graph of COVID-19 and predict the possibility of the occurrence of the derivative public opinion.The research results show the hot events and evolution direction of COVID-19 network public opinion in a clear way,and it can provide reference for the network regulatory department to implement intervention.