Read Online Reviews Research Articles

Optimisation of Sentiment Analysis for E-Commerce

Sentiment analysis is widely used today to make data-driven decisions in different industries, starting from marketing and including brand management, reputation monitoring, and customer satisfaction analysis. Its growing importance is closely linked with so-called ‘word-of-mouth’ communication, from reading online reviews to writing comments on social networks. Effective separation of sentiments ensures that companies' responses are timely and critical patterns are seen in big data sets. Statistical measures, information gain, correlation-based approaches, etc, have been employed for the feature selection. Still, the problem associated with text data mining is that they don’t convey the text's relative difficulty and additional features. To fill this gap, our research proposes a new feature selection technique through Ant Colony Optimization (ACO) and K Nearest Neighbour (KNN) performed on 28,000 customer reviews in different product categories. The results, therefore, showed an overall accuracy of 80.1%, with the Support Vector Machine (SVM) set at 80.5% on each selected feature, which was slightly higher than the Convolutional Neural Network (CNN), which scored a 78.41% accuracy. SVM remains on the mark of 83%, and for CNN, the rate achieved on the same was 80.8% when both were applied to the entire dataset. These facts rejected the infallibility of the simple and complex algorithms used singly in the sentiment classification, indicating that more sophisticated algorithms like ACO and KNN can provide business solutions to improve their service delivery based on customers’ feedback.

How to stay competitive: An innovative concept to assess the business competitiveness using online restaurant reviews

Nowadays customers would make decisions by reading online reviews and comparing differences in restaurants before visiting. Therefore how restaurants take advantage from such information is important to attract customers and stay competitive. Many researchers believe that increasing business performance could improve competitiveness. However, with changing customer requirements and business environment, it is challenging to understand which attributes matter the most to customers and how to improve considering competitors. Therefore this study proposed assessing restaurant competitiveness using online reviews. After crawling 38,479 online reviews employing Python, deep learning-based BERT is developed to measure attribute performance and understand the competitiveness through McKinsey Matrix. Then, the competitiveness was analyzed from a temporal dynamic view to present how attributes are changing importance. Notably, the asymmetric effects between attribute performance and satisfaction were considered. Results demonstrated encouraging accuracy in measuring restaurant competitiveness and explained how asymmetric McKinsey Matrix could help formulate efficient competitiveness enhancement strategies.

Online reviews and spillover effects in the dual-channel supply chain: A choice strategy of sales format

We investigate strategy choice of sales format considering online reviews and the spillover effect in a dual-channel supply chain. Generally, online and offline consumers read online reviews before purchasing, and thus offline retailers will be subjected to the spillover effect of online reviews. We develop joint product quality and service level decision models in reselling and agency selling with online reviews and the spillover effect. There are some interesting findings: first, the optimal service level and product quality increase with online reviews and spillover effect. Second, counterintuitively, the spillover effect of online reviews benefits not only the offline retailer and manufacturer but also the online retailer in some circumstances. Moreover, the win-win sales format for the manufacturer and online and offline retailers exists in some circumstances. Finally, it is interesting to find that consumers’ channel preference in the agency sales format is more important than in the resales.

Influence of Electronic Word-Of-Mouth on Restaurant Choice Decisions: Does It Depend on Gender in the Millennial Generation?

Given the exponential growth of eWOM, especially among the millennial generation, an analysis of the consultation behaviour of online opinions is essential to better understanding the decision-making process. The aim of this proposal is to analyse how the motivations towards eWOM consultation contribute to the final adoption of eWOM, especially in the restaurant context, exploring the relationship chain “motivations to consult eWOM—intention to consult eWOM—adoption to consult eWOM”. Moreover, studying the moderating effect of gender in this chain is argued. Based on a sample of 341 millennials with experience in reading online reviews and visiting restaurants, a causal model was estimated through PLS estimation in the geographic area of Ecuador. The results confirm that millennials’ motivations influence directly their intention to consult eWOM and indirectly on eWOM adoption. In addition, gender does not show a significant effect on the chain of effects. Given that virtual platforms have the potential to influence men and women equally, the communication efforts of restaurants focused on this target audience and carried out on social media must focus on aspects other than gender.

Online Review Influence on the Retail Industry

The research undertaken investigates the effect of online reviews on purchase emotions of young consumers with reference to National Capital Region of India. Quantitative aspects of online reviews, like reviewer quality, reviewer exposure, product attributes, and temporal effects have been considered. Based on literature review, the S-O-R model has been further built on and constructs of A.I.S.A.S. model, i.e., attention and trust, action and share have been incorporated to evaluate buying pattern and behaviour they follow related to online review. Interestingly, when consumers read online reviews, they concentrate not only on review scores but also refer to other textual information like a reviewer's trust and the content associated with it such as various pictures and videos. It is also seen that the market responds more favourably to reviews written by reviewers with better reputations and better exposure, i.e., the influencers. The study also aims to explore the strength of the relationship between the identified independent constructs of online reviews and buying behaviour of an individual.

Clustering family-friendly hotels’ guests to develop tourism marketing strategies

An increasing number of guests in hotels evaluate the quality by reading online reviews. A deeper analysis of the attitude and behavior of the visitors is conducted to understand the experiences of guests, considering the diverse backgrounds and needs. This study aims to analyze the selection process of family-friendly hotels by guests, using available TripAdvisor online reviews, as well as for hotel management to better understand the comments left by guests and create more organized plans and policies. A model is devised that integrates clustering and Multi-Criteria Decision-Making-VIKOR (MCDM-VIKOR) method to prioritize the attributes of hotels based on the significance within each cluster of guests. Data is collected from online reviews of guests in family-friendly hotels in Indonesia. The features used for ranking preferences are the numerical ratings assigned to four attributes on the platform. These four features included ?location?, ?cleanliness?, ?service?, and ?value?. The results showed that ?cleanliness? evolved as the most critical factor in the majority of segments for selecting family-friendly hotels. To further comprehend the behavioral trends of guests and assist in decision-making, this study proposed a model capable of analyzing online reviews and ratings provided by customers.

What leads to online review helpfulness on e-commerce platform? Examining the moderating effect of high and low prices

Using signaling theory, this study unpacked the mechanisms through which online review information quality and reviewer information credibility influence online review helpfulness, in e-commerce context. Conducting a survey with users in Danang having read online reviews on Shopee.vn, 244 valid responses were used to evaluate the research model via PLS-SEM software. The study found that the information quality of online reviews strongly influences the helpfulness of online reviews compared to the information credibility of previous reviewers. Additionally, priced goods have a moderating effect on the relationship between online information credibility and online reviews helpfulness; and that relationship will be significant in high-priced products. This study makes a theoretical contribution to online reviews by elucidating the mechanisms of impact of review/reviewer information on reviews helpfulness and the moderating effect of product prices.

The Impact of Contradictory Online Reviews on Consumer Online Purchase Decision: Experimental Evidence From China

Most consumers read online reviews before making online purchase decisions. Online reviews of a product usually include positive and negative contradictory online reviews (CORs). Previous studies have confirmed the impact of CORs on consumers’ online purchase decisions. However, literature review showed that most of the conducted studies on the dimension of CORs are single dimension. Therefore, this study examined how CORs affected consumers’ online purchase decisions from two dimensions: proportion of positive and negative reviews and emotional arousal. The results of a two (proportion of positive and negative reviews: high vs. low) × 2 (emotional arousal: calm vs. arousing) inter group experiment from 306 Chinese consumers showed that CORs have a significant negative impact on consumers’ purchase online decision, and CORs affect consumers’ online purchase decision by affecting perceived risk. The results of this study enrich literature of online reviews. Findings of this study provide important information to e-retailers on how CORs affect consumers’ online purchase decisions. The theoretical and practical implications of the proposed study are also discussed.

Customers online Reviews and Its impact on Purchase Intention in Hospitality Industry: A Quantitative Investigation

When booking an internet trip, many buyers read online reviews. The influence of these reviews on customer decision-making is still imperfectly understood. Online reviews largely impact the hospitality Industry as in the era of digitization everything is available at the fingertip, even the review.  Positive reviews change people's perceptions towards hotels, while both good and negative evaluations raise customer awareness of hotels. At less well-known hotels, these impacts are more pronounced. A reviewer competence is influenced by just a little amount of positive review. Several websites and applications offer online evaluations of hotels in well-known tourist locations in response to growing customer demand for impartial leisure - related information. According to reports, millions of prospective hotel guests check these review websites every year. Online reviews have a great impact on the hotels and hospitality industry. One of the fundamental requirements for tourists at a location is lodging; therefore, choosing a hotel is a top priority for most visitors. Understanding how users select lodging is essential in the increasingly cutthroat service sector of hospitality since it may help hoteliers and tourist managers with planning and decision-making.

Millennials Seeking Healthcare: Examining the Degree to Which Patients Utilize Online Resources.

IntroductionAccording to the 2020 U.S. Census, a Silver Tsunami is looming, with more than 75.4 million persons aged 57 to 75 expected to need more costly medical care. However, a larger wave of 83.1 million Millennials nearing adulthood is approaching rapidly. Therefore, it is important to understand how this population finds their physician and what may influence this decision.MethodsPaper-based surveys were administered to adult patients at primary care and geriatric clinics located at the University of Kansas Medical Center in Kansas City, Kansas. Questions included demographic information, utilization and influence of online reviews, and the effects negative and positive reviews have on a patient’s choice of physician. Descriptive statistics were calculated for respondent characteristics and survey responses. Chi-square and McNemar’s tests were performed to evaluate differences between age and gender groups, and to determine how influential review ratings are in choosing a physician for medical care. Statistical significance was determined at the 0.05 level.ResultsA sample of 284 patients completed the survey (44.35 ± 17.54 years old [range = 18–90], 60.6% female, 57.4% white). Of Millennials, 67.2% read online reviews before choosing a physician. Millennials were significantly more likely to read online reviews before choosing a physician (p = 0.004) and utilize online resources to search for a new physician (p < 0.001) than older patients.ConclusionsMillennials were more likely to research online reviews before choosing a physician. Therefore, an online review presence will be beneficial to one’s practice to acquire this new wave of patients.

Decide Now or Later: Making Sense of Incoherence Across Online Reviews

Consumers read online reviews to decide whether to buy a product. Extensive research examines what makes a single review helpful or credible, yet there is very limited understanding of how a collection of reviews facilitates purchase decisions. Such understanding is critical because consumers rarely consult all reviews or a single review. They often start by reading the “top reviews” that a website highlights, then deciding whether to read additional reviews and how many. This paper investigates how inconsistency among top reviews affects a consumer’s purchase deferral—the likelihood to decide immediately or defer the decision until after obtaining more information. We found that, if different reviewers disagree on their opinions about the same feature of a product, consumers are more likely to defer the purchase decision and consult more reviews. Further, this effect is weaker when reviewers provide specific details about their needs or use of the product along with their opinions. This work provides guidance to review platforms on how to select and present a set of top reviews. Our findings also inform retailers and product manufacturers on how to focus their attention in dealing with reviews and when a focus only on the top reviews is not sufficient.

A Quantitative Study of Software Reviews Using Content Analysis Methods

Online product reviews play a critical role for consumers to make decision of product purchasing, and become an important data source for vendors to build a recommendation system. Some consumers even won’t buy a product without reading online reviews first, and some vendors invite reviewers to write product reviews before a product is released to the market. However, review quality could great impact the useless of the reviews for supporting the purchasing decision and recommendation. In order to produce enough high-quality reviews, it is a normal practice that vendors provide incentives for writing product reviews. Some research work showed the incentive reviews could be more bias comparing to organic reviews, but other work showed incentive reviews contain more useful information. However, some other research work showed very different results regarding the differences between the incentivized reviews and organic reviews. One of the reasons explaining the observation differences could be due to the quality of the reviewers and the reviews. Therefore, it is necessary to control the quality of the reviewers and the reviews to more objectively comparing the two types of reviews. In this research, we first discuss an approach for ensuring the quality of the data collection and processing to ensure the quality of the comparison study. Then we explore the differences between incentivized reviews and organic reviews collected from a website that provides reviews for enterprise software systems. Several parameters of the reviews such as overall score and sentiment and subjectivity of the reviews were analyzed and compared. The research result could provide a reference for appropriately using the reviews and managing the reviewing process.

Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Most consumers read online reviews written by different users before making purchase decisions, where each opinion expresses some sentiment. Therefore, sentiment analysis is currently a hot topic of research. In particular, aspect-based sentiment analysis concerns the exploration of emotions, opinions and facts that are expressed by people, usually in the form of polarity. It is crucial to consider polarity calculations and not simply categorize reviews as positive, negative, or neutral. Currently, the available lexicon-based method accuracy is affected by limited coverage. Several of the available polarity estimation techniques are too general and may not reflect the aspect/topic in question if reviews contain a wide range of information about different topics. This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis (ABSA-PER). ABSA-PER has three major phases: data preprocessing, aspect co-occurrence calculation (CAC) and polarity estimation. A multi-domain sentiment dataset, Twitter dataset, and trust pilot forum dataset (developed by us by defined judgement rules) are used to verify ABSA-PER. Experimental outcomes show that ABSA-PER achieves better accuracy, i.e., 85.7% accuracy for aspect extraction and 86.5% accuracy in terms of polarity estimation, than that of the baseline methods.

Tales from the Land of Consumer Reviews: Taking a Closer Look at Lurkers and Writers

Abstract While most consumers routinely read online reviews to inform their purchase decisions, the number of consumers who actively post reviews is astonishingly small. To find their way in the review jungle, readers process the information presented very selectively and pursue different objectives. They use different informational cues for their own decision-making, which often reflects rather stable patterns. Writers, too, are often less interested in presenting their experience as objectively as possible as contextual factors play an important role. Similar to the review readers, they act neither uniformly nor without biases. Whether they take up the pen at all depends, among other things, on whether they represent a majority or minority opinion and what image of themselves they want to convey. A profound understanding of the motives of readers and writers is important to improve one’s own rating system and requires special skills in the management of online ratings. Managers need to find ways to convert more lurkers into posters to get a more realistic picture of the experiences.

Processes and methods of information fusion for ranking products based on online reviews: An overview

Over the past few years, more and more consumers have come to read online reviews when they shop online. To support consumers' purchase decisions, many scholars focus on ranking products based on online reviews and propose various methods and techniques. Generally, the process of information fusion for ranking products based on online reviews consists of three stages: product feature extraction, sentiment analysis, and ranking products. In this paper, we review the existing studies on processes and methods of information fusion for each stage. Furthermore, we briefly review the existing research on information fusion based on online reviews in other fields. Finally, we summarize the main conclusions of this paper and point out the future research direction.

Effective Selection of a Compact and High-Quality Review Set with Information Preservation

Consumers increasingly make informed buying decisions based on reading online reviews for products and services. Due to the large volume of available online reviews, consumers hardly have the time and patience to read them all. This article aims to select a compact set of high-quality reviews that can cover a specific set of product features and related consumer sentiments. Selecting such a subset of reviews can significantly save the time spent on reading reviews while preserving the information needed. A unique review selection problem is defined and modeled as a bi-objective combinatorial optimization problem, which is then transformed into a minimum-cost set cover problem that is NP-complete. Several approximation algorithms are then designed, which can sustain performance guarantees in polynomial time. Our effective selection algorithms can also be upgraded to handle dynamic situations. Comprehensive experiments conducted on twelve real datasets demonstrate that the proposed algorithms significantly outperform benchmark methods by generating a more compact review set with much lower computational cost. The number of reviews selected is much smaller compared to the quantity of all available reviews, and the selection efficiency is deeply increased by accelerating strategies, making it very practical to adopt the methods in real-world online applications.

Bayesian Social Learning from Consumer Reviews

When buying a new product online, potential consumers often read online reviews to get an understanding of the quality of the product, and then they decide whether to make a purchase. In turn, people who purchase the product submit reviews to the aggregator site, thus adding new information over time. This paper is the first to study such a social learning mechanism based on online reviews under the assumption that consumers are Bayesian. It identifies assumptions under which consumers eventually learn the true quality and explores the optimal pricing policy for a seller who participates in such a market.

Managing Customer Reviews for Value Co-creation: An Empowerment Theory Perspective

Tourism provides myriad opportunities for customer engagement and value co-creation, especially in online communities. This research analyzed the role of empowerment in the tourist knowledge value co-creation process in online review contexts. Using scenario-based, between-subjects experimental designs, three studies were conducted using online consumer samples to examine the effects of hotel response personalization and online review valence on empowerment and intention to co-create knowledge value for both previous and prospective hotel guests. The studies find that online reviewers (previous guests) are more empowered when they receive a personalized response and when they have positive service experiences. The effect of hotel response personalization on empowerment is stronger in negative as compared to positive review scenarios. Empowerment mediates the effect of personalization on intentions to co-create knowledge value. The same effects exist for prospective guests who read online reviews and hotel responses. Theoretical and practical implications are discussed.

THE INFLUENCE OF ELECTRONIC WORD OF MOUTH TOWARD DESTINATION TRUST AND TRAVEL INTENTION ON SIRANDAH ISLAND IN PADANG CITY

This research aims to find out: (1) The influence of electronic word of mouth toward travel intention on Sirandah Island in Padang City (2) The influence of electronic word of mouth toward destination trust on Sirandah Island in Padang City (3) The influence of destination trust toward travel intention on Sirandah Island in Padang City. The type of this research is causative research. The population on this research is all Padang city society who have read online reviews of Sirandah Island in the internet and have never visited Sirandah Island whose numbers are unknwon. Data were collected through questionnaires and 170 questionnaires were analyzed by performing structural equation modeling (SEM) in SmartPLS 3.2.7. The result of this research show that (1) electronic word of mouth has a significant influenced on travel intention at Sirandah Island in Padang City (2) electronic word of mouth has a significant influenced on destination trust at Sirandah Island in Padang City (3) destination trust has a significant influenced on travel intention at Sirandah Island in PadangCity.Keyword: Electronic Word of Mouth, Destination Trust, Travel Intention

I see, and I hunt

PurposeDespite the growth of social networking websites as hybrid promotional tools, the empirical research that is currently available in explaining the consequences of gastronomy online reviews on ethnic food behavioural intention is limited. The purpose of this paper is to adopt a novel approach to explore Gen-Y tourists’ behavioural intention by proposing “involvement” as a mediator in improvising Wang et al.’s model. In other words, the more tourists enjoy reading gastronomy online reviews and gain benefits like inspiring taste desire, forming taste awareness and facilitating interpersonal interaction, the more they will become actively involved in searching gastronomy online reviews for ethnic food, subsequently manifesting in higher behavioural intention.Design/methodology/approachTo test the research model, a questionnaire was developed and distributed to Gen-Y tourists in Malaysia using the purposive sampling technique. Partial least squares–structural equation modelling was applied to analyse the data collected from a sample of 748 responses.FindingsThis study provides empirical findings based on the direct relationship between gastronomy online review dimensions and the behavioural intention towards ethnic food. In addition, the indirect influence of the constructs for behavioural intention through involvement and the moderating effect of variety seeking were found. Indeed, when involvement increases as a result of reading online reviews, behavioural intention is enhanced. In fact, if the individual is a variety seeker, the behavioural intention could likewise be enhanced.Practical implicationsThis paper provides suggestions and valuable insight for tourism authorities and ethnic food eateries in using gastronomy online reviews for promoting ethnic food and gastronomy tourism. For example, food eateries can motivate reviewers to include attractive and useful content by facilitating how the gastronomy reviewer retrieves information (e.g. ingredients, price, and food health value and food tradition) concerning the eatery. Additionally, rewards and incentives such as offering vouchers to reviewers in order to gain attractive and interesting reviews could be considered. Finally, the variety-seeking goal of tourists can be attained by relocating ethnic food restaurants or stalls at centralised or designated areas, where a variety of ethnic foods are conveniently accessible to tourists.Originality/valueThis paper systematically discusses how gastronomy online reviews influence the behavioural intention of tourists to taste ethnic food in the context of Malaysia. The inclusion of the mediator (involvement) and moderator (variety seeking) added a significant increment to the total variance explained in behavioural intention.