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reviews</title> <meta name="description" content="Search results for: online user reviews"> <meta name="keywords" content="online user reviews"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="online user reviews"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5343</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: online user reviews</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5343</span> Lying Decreases Relying: Deceiver's Distrust in Online Restaurant Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jenna%20Barriault">Jenna Barriault</a>, <a href="https://publications.waset.org/abstracts/search?q=Reeshma%20Haji"> Reeshma Haji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online consumer behaviourand reliance on online reviews may be more pervasive than ever, andthis necessitates a better scientific understanding of the widespread phenomenon of online deception. The present research focuses on the understudied topic of deceiver’s distrust, where those who engage in deception later have less trust in others in the context of online restaurant reviews. The purpose was to examine deception and valence in online restaurant reviews and the effects they had on deceiver’s distrust. Undergraduate university students (N = 76) completed an online study where valence was uniquely manipulated by telling participants that either positive (or negative reviews) were influential and asking them to write a correspondingly valenced review. Deception was manipulated in the same task. Participants in the deception condition were asked to write an online restaurant review that was counter to their actual experience of the restaurant (negative review of a restaurant they liked, positive review of the restaurant they did not like). In the no deception condition, participants were asked to write a review that they actually liked or didn’t like (based on the valence condition to which they were randomly assigned). Participants’ trust was then assessed through various measures, includingfuture reliance on online reviews. There was a main effect of deception on reliance on online reviews. Consistent with deceiver’s distrust, those who deceived reported that they would rely less on online reviews. This study demonstrates that even when participants are induced to write a deceptive review, it can result in deceiver’s distrust, thereby lowering their trust in online reviews. If trust or reliance can be altered through deception in online reviews, people may start questioning the objectivity or true representation of a company based on such reviews. A primary implication is that people may reduce theirreliance upon online reviews if they know they are easily subject to manipulation. The findings of this study also contribute to the limited research regarding deceiver’s distrust in an online context, and further research is clarifying the specific conditions in which it is most likely to occur. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deceiver%E2%80%99s%20distrust" title="deceiver’s distrust">deceiver’s distrust</a>, <a href="https://publications.waset.org/abstracts/search?q=deception" title=" deception"> deception</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20reviews" title=" online reviews"> online reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=valence" title=" valence"> valence</a> </p> <a href="https://publications.waset.org/abstracts/146744/lying-decreases-relying-deceivers-distrust-in-online-restaurant-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146744.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5342</span> Exploring the Relationship Between Past and Present Reviews: The Influence of User Generated Content on Future Hotel Guest Experience Perceptions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sacha%20Joseph-Mathews">Sacha Joseph-Mathews</a>, <a href="https://publications.waset.org/abstracts/search?q=Leili%20Javadpour"> Leili Javadpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the tourism industry, hoteliers spend millions annually on marketing and positioning efforts for their respective hotels, all in an effort to create a specific image in the minds of the consumer. Yet despite extensive efforts to seduce potential hotel guests with sophisticated advertising messages generated by hotel entities, consumers continue to mistrust corporate branding, preferring instead to place their trust in the reviews of their consumer peers. In today’s complex and cluttered marketplace, online reviews can serve as a mediator for consumers who do not have actual knowledge and experiences with the brand, but are in the process of deciding whether or not to engage in a consumption exercise. Traditionally, consumers have used online reviews as a source of comfort and confirmation of a product/service’s positioning. But today, very few customers make any purchase decisions without first researching existing user reviews, making reviews more of a necessity, rather than a luxury in the purchase decision process. The influence of user generated content (UGC) is amplified in the tourism industry; as more than a third of potential hotel guests will not book a room without first reading a review. As corporate branding becomes less relevant and online reviews become more important, how much of the consumer’s stay expectations are being dictated by existing UGC? Moreover, as hotel guest experience a hotel through the lens of an existing review, how much of their stay and in turn their review, would have been influenced by those reviews that they read? Ultimately, there is the potential for UGC to dictate what potential guests will be most critical about, and or most focused on during their stay. If UGC is a stronger influencer in the purchase decision process than corporate branding, doesn’t it have the potential to dictate, the entire stay experience by influencing the expectations of the guest prior to them arriving on the property? For example, if a hotel is an eco-destination and they focus their branding on their website around sustainability and the retreat nature of the hotel. Yet, guest reviews constantly discuss how dissatisfactory the service and food was with no mention of nature or sustainability, will future reviews then focus primarily on the food? Using text analysis software to examine over 25,000 online reviews, we explore the extent to which new reviews are influenced by wording used in previous reviews for a hotel property, versus content generated by corporate positioning. Additionally, we investigate how distinct hotel related UGC is across different types of tourism destinations. Our findings suggest that UGC can have a greater impact on future reviews, than corporate branding and there is more cohesiveness across UGC of different types of hotel properties than anticipated. A model of User Generated Content Influence is presented and the managerial impact of the power of online reviews to trump corporate branding and shape future user experiences is discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=user%20generated%20content" title="user generated content">user generated content</a>, <a href="https://publications.waset.org/abstracts/search?q=UGC" title=" UGC"> UGC</a>, <a href="https://publications.waset.org/abstracts/search?q=corporate%20branding" title=" corporate branding"> corporate branding</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20reviews" title=" online reviews"> online reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=hotels%20and%20tourism" title=" hotels and tourism"> hotels and tourism</a> </p> <a href="https://publications.waset.org/abstracts/167186/exploring-the-relationship-between-past-and-present-reviews-the-influence-of-user-generated-content-on-future-hotel-guest-experience-perceptions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167186.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5341</span> Leveraging Sentiment Analysis for Quality Improvement in Digital Healthcare Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naman%20Jain">Naman Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaun%20Fernandes"> Shaun Fernandes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing prevalence of online healthcare services, selecting the most suitable doctor has become a complex task, requiring careful consideration of both public sentiment and personal preferences. This paper proposes a sentiment analysis-driven method that integrates public reviews with user-specific criteria and correlated attributes to recommend online doctors. By leveraging Natural Language Processing (NLP) techniques, public sentiment is extracted from online reviews, which is then combined with user-defined preferences such as specialty, years of experience, location, and consultation fees. Additionally, correlated attributes like education and certifications are incorporated to enhance the recommendation accuracy. Experimental results demonstrate that the proposed system significantly improves user satisfaction by providing personalized doctor recommendations that align with both public opinion and individual needs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20doctors" title=" online doctors"> online doctors</a>, <a href="https://publications.waset.org/abstracts/search?q=personal%20preferences" title=" personal preferences"> personal preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=correlated%20attributes" title=" correlated attributes"> correlated attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=healthcare" title=" healthcare"> healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/194882/leveraging-sentiment-analysis-for-quality-improvement-in-digital-healthcare-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">4</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5340</span> Emotion Oriented Students' Opinioned Topic Detection for Course Reviews in Massive Open Online Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhi%20Liu">Zhi Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xian%20Peng"> Xian Peng</a>, <a href="https://publications.waset.org/abstracts/search?q=Monika%20Domanska"> Monika Domanska</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingyun%20Kang"> Lingyun Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sannyuya%20Liu"> Sannyuya Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Massive Open education has become increasingly popular among worldwide learners. An increasing number of course reviews are being generated in Massive Open Online Course (MOOC) platform, which offers an interactive feedback channel for learners to express opinions and feelings in learning. These reviews typically contain subjective emotion and topic information towards the courses. However, it is time-consuming to artificially detect these opinions. In this paper, we propose an emotion-oriented topic detection model to automatically detect the students’ opinioned aspects in course reviews. The known overall emotion orientation and emotional words in each review are used to guide the joint probabilistic modeling of emotion and aspects in reviews. Through the experiment on real-life review data, it is verified that the distribution of course-emotion-aspect can be calculated to capture the most significant opinioned topics in each course unit. This proposed technique helps in conducting intelligent learning analytics for teachers to improve pedagogies and for developers to promote user experiences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Massive%20Open%20Online%20Course%20%28MOOC%29" title="Massive Open Online Course (MOOC)">Massive Open Online Course (MOOC)</a>, <a href="https://publications.waset.org/abstracts/search?q=course%20reviews" title=" course reviews"> course reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title=" emotion recognition"> emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topical%20aspects" title=" topical aspects"> topical aspects</a> </p> <a href="https://publications.waset.org/abstracts/86771/emotion-oriented-students-opinioned-topic-detection-for-course-reviews-in-massive-open-online-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86771.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5339</span> Lexical Features and Motivations of Product Reviews on Selected Philippine Online Shops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jimmylen%20Tonio">Jimmylen Tonio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Anudin"> Ali Anudin</a>, <a href="https://publications.waset.org/abstracts/search?q=Rochelle%20Irene%20G.%20Lucas"> Rochelle Irene G. Lucas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Alongside the progress of electronic-business websites, consumers have become more comfortable with online shopping. It has become customary for consumers that prior to purchasing a product or availing services, they consult online reviews info as bases in evaluating and deciding whether or not they should push thru with their procurement of the product or service. Subsequently, after purchasing, consumers tend to post their own comments of the product in the same e-business websites. Because of this, product reviews (PRS) have become an indispensable feature in online businesses equally beneficial for both business owners and consumers. This study explored the linguistic features and motivations of online product reviews on selected Philippine online shops, LAZADA and SHOPEE. Specifically, it looked into the lexical features of the PRs, the factors that motivated consumers to write the product reviews, and the difference of lexical preferences between male and female when they write the reviews. The findings revealed the following: 1. Formality of words in online product reviews primarily involves non-standard spelling, followed by abbreviated word forms, colloquial contractions and use of coined/novel words; 2. Paralinguistic features in online product reviews are dominated by the use of emoticons, capital letters and punctuations followed by the use of pictures/photos and lastly, by paralinguistic expressions; 3. The factors that motivate consumers to write product reviews varied. Online product reviewers are predominantly driven by venting negative feelings motivation, followed by helping the company, helping other consumers, positive self-enhancement, advice seeking and lastly, by social benefits; and 4. Gender affects the word frequencies of product online reviews, while negation words, personal pronouns, the formality of words, and paralinguistic features utilized by both male and female online product reviewers are not different. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lexical%20choices" title="lexical choices">lexical choices</a>, <a href="https://publications.waset.org/abstracts/search?q=motivation" title=" motivation"> motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20shop" title=" online shop"> online shop</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20reviews" title=" product reviews"> product reviews</a> </p> <a href="https://publications.waset.org/abstracts/89139/lexical-features-and-motivations-of-product-reviews-on-selected-philippine-online-shops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89139.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5338</span> Factors Determining the Purchasing Intentions towards Online Shopping: An Evidence from Twin Cities of Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Waiz">Muhammad Waiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Maruf%20Tahir"> Rana Maruf Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Javaid"> Fatima Javaid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technology in the recent times is available for everyone in the world that no one is left behind. After getting technology into our daily routine, there is a need to study the different factors regarding online shopping. This study examines the impact of online reviews, mobile shopping and computer literacy on online purchasing intention. The sample size was 200 from which 167 complete questionnaires were collected from students and employees of twin cities. SPSS programming software was used to analyze the impact of different factors on purchasing intention. The results of this study showed that those websites which have good ratings and have online shopping application will attract more customers towards them whereas the results showed that the computer literacy has no impact on online purchasing intention. Findings may help for those who want to increase their sales or to start a new online business. Future research, limitations, and implications are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20literacy" title="computer literacy">computer literacy</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20shopping" title=" mobile shopping"> mobile shopping</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20purchase%20intention" title=" online purchase intention"> online purchase intention</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20reviews" title=" online reviews"> online reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=theory%20of%20planned%20behavior" title=" theory of planned behavior"> theory of planned behavior</a> </p> <a href="https://publications.waset.org/abstracts/88469/factors-determining-the-purchasing-intentions-towards-online-shopping-an-evidence-from-twin-cities-of-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">226</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5337</span> Analyzing Semantic Feature Using Multiple Information Sources for Reviews Summarization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Hung%20Chiang">Yu Hung Chiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hei%20Chia%20Wang"> Hei Chia Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title="text mining">text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20feature%20extraction" title=" product feature extraction"> product feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-lexicons" title=" multi-lexicons"> multi-lexicons</a> </p> <a href="https://publications.waset.org/abstracts/41662/analyzing-semantic-feature-using-multiple-information-sources-for-reviews-summarization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41662.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">331</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5336</span> A Supervised Approach for Detection of Singleton Spam Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atefeh%20Heydari">Atefeh Heydari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadali%20Tavakoli"> Mohammadali Tavakoli</a>, <a href="https://publications.waset.org/abstracts/search?q=Naomie%20Salim"> Naomie Salim </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title="classification algorithms">classification algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Na%C3%AFve%20Bayes" title=" Naïve Bayes"> Naïve Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20review%20spam%20detection" title=" opinion review spam detection"> opinion review spam detection</a>, <a href="https://publications.waset.org/abstracts/search?q=singleton%20review%20spam%20detection" title=" singleton review spam detection"> singleton review spam detection</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/36387/a-supervised-approach-for-detection-of-singleton-spam-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36387.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5335</span> Role of Gender in Apparel Stores' Consumer Review: A Sentiment Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sarif%20Ullah%20Patwary">Sarif Ullah Patwary</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Heinrich"> Matthew Heinrich</a>, <a href="https://publications.waset.org/abstracts/search?q=Brandon%20Payne"> Brandon Payne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ubiquity of web 2.0 platforms, in the form of wikis, social media (e.g., Facebook, Twitter, etc.) and online review portals (e.g., Yelp), helps shape today’s apparel consumers’ purchasing decision. Online reviews play important role towards consumers’ apparel purchase decision. Each of the consumer reviews carries a sentiment (positive, negative or neutral) towards products. Commercially, apparel brands and retailers analyze sentiment of this massive amount of consumer review data to update their inventory and bring new products in the market. The purpose of this study is to analyze consumer reviews of selected apparel stores with a view to understand, 1) the difference of sentiment expressed through men’s and woman’s text reviews, 2) the difference of sentiment expressed through men’s and woman’s star-based reviews, and 3) the difference of sentiment between star-based reviews and text-based reviews. A total of 9,363 reviews (1,713 men and 7,650 women) were collected using Yelp Dataset Challenge. Sentiment analysis of collected reviews was carried out in two dimensions: star-based reviews and text-based reviews. Sentiment towards apparel stores expressed through star-based reviews was deemed: 1) positive for 3 or 4 stars 2) negative for 1 or 2 stars and 3) neutral for 3 stars. Sentiment analysis of text-based reviews was carried out using Bing Liu dictionary. The analysis was conducted in IPyhton 5.0. Space. The sentiment analysis results revealed the percentage of positive text reviews by men (80%) and women (80%) were identical. Women reviewers (12%) provided more neutral (e.g., 3 out of 5 stars) star reviews than men (6%). Star-based reviews were more negative than the text-based reviews. In other words, while 80% men and women wrote positive reviews for the stores, less than 70% ended up giving 4 or 5 stars in those reviews. One of the key takeaways of the study is that star reviews provide slightly negative sentiment of the consumer reviews. Therefore, in order to understand sentiment towards apparel products, one might need to combine both star and text aspects of consumer reviews. This study used a specific dataset consisting of selected apparel stores from particular geographical locations (the information was not given for privacy concern). Future studies need to include more data from more stores and locations to generalize the findings of the study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=apparel" title="apparel">apparel</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20review" title=" consumer review"> consumer review</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=gender" title=" gender"> gender</a> </p> <a href="https://publications.waset.org/abstracts/101150/role-of-gender-in-apparel-stores-consumer-review-a-sentiment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5334</span> User-Based Cannibalization Mitigation in an Online Marketplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vivian%20Guo">Vivian Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Qu"> Yan Qu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cannibalization" title="cannibalization">cannibalization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20marketplace" title=" online marketplace"> online marketplace</a>, <a href="https://publications.waset.org/abstracts/search?q=revenue%20optimization" title=" revenue optimization"> revenue optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20optimization" title=" yield optimization"> yield optimization</a> </p> <a href="https://publications.waset.org/abstracts/82203/user-based-cannibalization-mitigation-in-an-online-marketplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">160</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5333</span> A Survey of Online User Perspectives and Age Profile in an Undergraduate Fundamental Business Technology Course</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danielle%20Morin">Danielle Morin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jennifer%20D.%20E.%20Thomas"> Jennifer D. E. Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=Raafat%20G.%20Saade"> Raafat G. Saade</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniela%20Petrachi"> Daniela Petrachi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few decades, more and more students choose to enroll in online classes instead of attending in-class lectures. While past studies consider students’ attitudes towards online education and how their grades differed from in-class lectures, the profile of the online student remains a blur. To shed light on this, an online survey was administered to about 1,500 students enrolled in an undergraduate Fundamental Business Technology course at a Canadian University. The survey was comprised of questions on students’ demographics, their reasons for choosing online courses, their expectations towards the course, the communication channels they use for the course with fellow students and with the instructor. This paper focused on the research question: Do the perspectives of online students concerning the online experience, in general, and in the course in particular, differ according to age profile? After several statistical analyses, it was found that age does have an impact on the reasons why students select online classes instead of in-class. For example, it was found that the perception that an online course might be easier than in-class delivery was a more important reason for younger students than for older ones. Similarly, the influence of friends is much more important for younger students, than for older students. Similar results were found when analyzing students’ expectation about the online course and their use of communication tools. Overall, the age profile of online users had an impact on reasons, expectations and means of communication in an undergraduate Fundamental Business Technology course. It is left to be seen if this holds true across other courses, graduate and undergraduate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20channels" title="communication channels">communication channels</a>, <a href="https://publications.waset.org/abstracts/search?q=fundamentals%20of%20business%20technology" title=" fundamentals of business technology"> fundamentals of business technology</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20classes" title=" online classes"> online classes</a>, <a href="https://publications.waset.org/abstracts/search?q=pedagogy" title=" pedagogy"> pedagogy</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20age%20profile" title=" user age profile"> user age profile</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20perspectives" title=" user perspectives"> user perspectives</a> </p> <a href="https://publications.waset.org/abstracts/86795/a-survey-of-online-user-perspectives-and-age-profile-in-an-undergraduate-fundamental-business-technology-course" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86795.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5332</span> A Long Tail Study of eWOM Communities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Olmedilla">M. Olmedilla</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Martinez-Torres"> M. R. Martinez-Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20L.%20Toral"> S. L. Toral</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=eWOM" title="eWOM">eWOM</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20user%20reviews" title=" online user reviews"> online user reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20tail%20theory" title=" long tail theory"> long tail theory</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20categorization" title=" product categorization"> product categorization</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a> </p> <a href="https://publications.waset.org/abstracts/21450/a-long-tail-study-of-ewom-communities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21450.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">421</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5331</span> Blame Classification through N-Grams in E-Commerce Customer Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhadeep%20Mandal">Subhadeep Mandal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujoy%20Bhattacharya"> Sujoy Bhattacharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Pabitra%20Mitra"> Pabitra Mitra</a>, <a href="https://publications.waset.org/abstracts/search?q=Diya%20Guha%20Roy"> Diya Guha Roy</a>, <a href="https://publications.waset.org/abstracts/search?q=Seema%20Bhattacharya"> Seema Bhattacharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title="e-commerce">e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20shopping" title=" online shopping"> online shopping</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20reviews" title=" customer reviews"> customer reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=customer%20behaviour" title=" customer behaviour"> customer behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20analytics" title=" text analytics"> text analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=n-grams%20classification" title=" n-grams classification"> n-grams classification</a> </p> <a href="https://publications.waset.org/abstracts/168088/blame-classification-through-n-grams-in-e-commerce-customer-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168088.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5330</span> The Role of Logistics Services in Influencing Customer Satisfaction and Reviews in an Online Marketplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=nafees%20mahbub">nafees mahbub</a>, <a href="https://publications.waset.org/abstracts/search?q=blake%20tindol"> blake tindol</a>, <a href="https://publications.waset.org/abstracts/search?q=utkarsh%20shrivastava"> utkarsh shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=kuanchin%20chen"> kuanchin chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online shopping has become an integral part of businesses today. Big players such as Amazon are setting the bar for delivery services, and many businesses are working towards meeting them. However, what happens if a seller underestimates or overestimates the delivery time? Does it translate to consumer comments, ratings, or lost sales? Although several prior studies have investigated the impact of poor logistics on customer satisfaction, that impact of under estimation of delivery times has been rarely considered. The study uses real-time customer online purchase data to study the impact of missed delivery times on satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LOST%20SALES" title="LOST SALES">LOST SALES</a>, <a href="https://publications.waset.org/abstracts/search?q=DELIVERY%20TIME" title="DELIVERY TIME">DELIVERY TIME</a>, <a href="https://publications.waset.org/abstracts/search?q=CUSTOMER%20SATISFACTION" title="CUSTOMER SATISFACTION">CUSTOMER SATISFACTION</a>, <a href="https://publications.waset.org/abstracts/search?q=CUSTOMER%20REVIEWS" title="CUSTOMER REVIEWS">CUSTOMER REVIEWS</a> </p> <a href="https://publications.waset.org/abstracts/143670/the-role-of-logistics-services-in-influencing-customer-satisfaction-and-reviews-in-an-online-marketplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">214</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5329</span> Neural Networks Models for Measuring Hotel Users Satisfaction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asma%20Ameur">Asma Ameur</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhafer%20Malouche"> Dhafer Malouche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, user comments on the Internet have an important impact on hotel bookings. This confirms that the e-reputation issue can influence the likelihood of customer loyalty to a hotel. In this way, e-reputation has become a real differentiator between hotels. For this reason, we have a unique opportunity in the opinion mining field to analyze the comments. In fact, this field provides the possibility of extracting information related to the polarity of user reviews. This sentimental study (Opinion Mining) represents a new line of research for analyzing the unstructured textual data. Knowing the score of e-reputation helps the hotelier to better manage his marketing strategy. The score we then obtain is translated into the image of hotels to differentiate between them. Therefore, this present research highlights the importance of hotel satisfaction ‘scoring. To calculate the satisfaction score, the sentimental analysis can be manipulated by several techniques of machine learning. In fact, this study treats the extracted textual data by using the Artificial Neural Networks Approach (ANNs). In this context, we adopt the aforementioned technique to extract information from the comments available in the ‘Trip Advisor’ website. This actual paper details the description and the modeling of the ANNs approach for the scoring of online hotel reviews. In summary, the validation of this used method provides a significant model for hotel sentiment analysis. So, it provides the possibility to determine precisely the polarity of the hotel users reviews. The empirical results show that the ANNs are an accurate approach for sentiment analysis. The obtained results show also that this proposed approach serves to the dimensionality reduction for textual data’ clustering. Thus, this study provides researchers with a useful exploration of this technique. Finally, we outline guidelines for future research in the hotel e-reputation field as comparing the ANNs with other technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20behavior" title=" consumer behavior"> consumer behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=e-reputation" title=" e-reputation"> e-reputation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20hotel%20%E2%80%98reviews" title=" online hotel ‘reviews"> online hotel ‘reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title=" opinion mining"> opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=scoring" title=" scoring"> scoring</a> </p> <a href="https://publications.waset.org/abstracts/97372/neural-networks-models-for-measuring-hotel-users-satisfaction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97372.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5328</span> Privacy Label: An Alternative Approach to Present Privacy Policies from Online Services to the User</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Roberto%20Goncalves%20De%20Pontes">Diego Roberto Goncalves De Pontes</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Donizetti%20Zorzo"> Sergio Donizetti Zorzo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Studies show that most users do not read privacy policies from the online services they use. Some authors claim that one of the main causes of this is that policies are long and usually hard to understand, which make users lose interest in reading them. In this scenario, users may agree with terms without knowing what kind of data is being collected and why. Given that, we aimed to develop a model that would present the privacy policies contents in an easy and graphical way for the user to understand. We call it the Privacy Label. Using information recovery techniques, we propose an architecture that is able to extract information about what kind of data is being collected and to what end in the policies and show it to the user in an automated way. To assess our model, we calculated the precision, recall and f-measure metrics on the information extracted by our technique. The results for each metric were 68.53%, 85.61% e 76,13%, respectively, making it possible for the final user to understand which data was being collected without reading the whole policy. Also, our proposal can facilitate the notice-and-choice by presenting privacy policy information in an alternative way for online users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=privacy" title="privacy">privacy</a>, <a href="https://publications.waset.org/abstracts/search?q=policies" title=" policies"> policies</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20behavior" title=" user behavior"> user behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20human%20interaction" title=" computer human interaction"> computer human interaction</a> </p> <a href="https://publications.waset.org/abstracts/51017/privacy-label-an-alternative-approach-to-present-privacy-policies-from-online-services-to-the-user" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">307</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5327</span> Altruistic and Hedonic Motivations to Write eWOM Reviews on Hotel Experience</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Llorens-Marin">Miguel Llorens-Marin</a>, <a href="https://publications.waset.org/abstracts/search?q=Adolfo%20Hernandez"> Adolfo Hernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Puelles-Gallo"> Maria Puelles-Gallo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The increasing influence of Online Travel Agencies (OTAs) on hotel bookings and the electronic word-of-mouth (eWOM) contained in them has been featured by many scientific studies as a major factor in the booking decision. The main reason is that nowadays, in the hotel sector, consumers first come into contact with the offer through the web and the online environment. Due to the nature of the hotel product and the fact that it is booked in advance to actually seeing it, there is a lack of knowledge about its actual features. This makes eWOM a major channel to help consumers to reduce their perception of risk when making their booking decisions. This research studies the relationship between aspects of customer influenceability by reading eWOM communications, at the time of booking a hotel, with the propensity to write a review. In other words, to test relationships between the reading and the writing of eWOM. Also investigates the importance of different underlying motivations for writing eWOM. Online surveys were used to obtain the data from a sample of hotel customers, with 739 valid questionnaires. A measurement model and Path analysis were carried out to analyze the chain of relationships among the independent variable (influenceability from reading reviews) and the dependent variable (propensity to write a review) with the mediating effects of additional variables, which help to explain the relationship. The authors also tested the moderating effects of age and gender in the model. The study considered three different underlying motivations for writing a review on a hotel experience, namely hedonic, altruistic and conflicted. Results indicate that the level of influenceability by reading reviews has a positive effect on the propensity to write reviews; therefore, we manage to link the reading and the writing of reviews. Authors also discover that the main underlying motivation to write a hotel review is the altruistic motivation, being the one with the higher Standard regression coefficient above the hedonic motivation. The authors suggest that the propensity to write reviews is not related to sociodemographic factors (age and gender) but to attitudinal factors such as ‘the most influential factor when reading’ and ‘underlying motivations to write. This gives light on the customer engagement motivations to write reviews. The implications are that managers should encourage their customers to write eWOM reviews on altruistic grounds to help other customers to make a decision. The most important contribution of this work is to link the effect of reading hotel reviews with the propensity to write reviews. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hotel%20reviews" title="hotel reviews">hotel reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20word-of-mouth%20%28eWOM%29" title=" electronic word-of-mouth (eWOM)"> electronic word-of-mouth (eWOM)</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20consumer%20reviews" title=" online consumer reviews"> online consumer reviews</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20marketing" title=" digital marketing"> digital marketing</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/158059/altruistic-and-hedonic-motivations-to-write-ewom-reviews-on-hotel-experience" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158059.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">100</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5326</span> Mobile Health Programs by Government: A Content Analysis of Online Consumer Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ge%20Zhan">Ge Zhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile health (mHealth) concerns the use of mobile technologies to deliver health care and improve wellness. In this paper, we ask the question of what are the drivers of positive consumer attitude toward mHealth programs. Answers to this question are important to consumer health, but existing marketing and health care service literature does not provide sufficient empirical conclusions on the use of mobile technologies for consumer health. This study aims to fill the knowledge gap by investigating mHealth use and consumer attitude. A content analysis was conducted with sample mHealth programs and online consumer reviews in Hong Kong, UK, US, and India. The research findings will contribute to marketing and health services literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20health" title="mobile health">mobile health</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20attitude" title=" consumer attitude"> consumer attitude</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20marketing" title=" online marketing"> online marketing</a> </p> <a href="https://publications.waset.org/abstracts/41497/mobile-health-programs-by-government-a-content-analysis-of-online-consumer-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41497.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5325</span> Artificial Intelligence Assisted Sentiment Analysis of Hotel Reviews Using Topic Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sushma%20Ghogale">Sushma Ghogale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With a surge in user-generated content or feedback or reviews on the internet, it has become possible and important to know consumers' opinions about products and services. This data is important for both potential customers and businesses providing the services. Data from social media is attracting significant attention and has become the most prominent channel of expressing an unregulated opinion. Prospective customers look for reviews from experienced customers before deciding to buy a product or service. Several websites provide a platform for users to post their feedback for the provider and potential customers. However, the biggest challenge in analyzing such data is in extracting latent features and providing term-level analysis of the data. This paper proposes an approach to use topic modeling to classify the reviews into topics and conduct sentiment analysis to mine the opinions. This approach can analyse and classify latent topics mentioned by reviewers on business sites or review sites, or social media using topic modeling to identify the importance of each topic. It is followed by sentiment analysis to assess the satisfaction level of each topic. This approach provides a classification of hotel reviews using multiple machine learning techniques and comparing different classifiers to mine the opinions of user reviews through sentiment analysis. This experiment concludes that Multinomial Naïve Bayes classifier produces higher accuracy than other classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=latent%20Dirichlet%20allocation" title="latent Dirichlet allocation">latent Dirichlet allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20modeling" title=" topic modeling"> topic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a> </p> <a href="https://publications.waset.org/abstracts/132279/artificial-intelligence-assisted-sentiment-analysis-of-hotel-reviews-using-topic-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132279.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5324</span> Cultural Dynamics in Online Consumer Behavior: Exploring Cross-Country Variances in Review Influence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eunjung%20Lee">Eunjung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research investigates the intricate connection between cultural differences and online consumer behaviors by integrating Hofstede's Cultural Dimensions theory with analysis methodologies such as text mining, data mining, and topic analysis. Our aim is to provide a comprehensive understanding of how national cultural differences influence individuals' behaviors when engaging with online reviews. To ensure the relevance of our investigation, we systematically analyze and interpret the cultural nuances influencing online consumer behaviors, especially in the context of online reviews. By anchoring our research in Hofstede's Cultural Dimensions theory, we seek to offer valuable insights for marketers to tailor their strategies based on the cultural preferences of diverse global consumer bases. In our methodology, we employ advanced text mining techniques to extract insights from a diverse range of online reviews gathered globally for a specific product or service like Netflix. This approach allows us to reveal hidden cultural cues in the language used by consumers from various backgrounds. Complementing text mining, data mining techniques are applied to extract meaningful patterns from online review datasets collected from different countries, aiming to unveil underlying structures and gain a deeper understanding of the impact of cultural differences on online consumer behaviors. The study also integrates topic analysis to identify recurring subjects, sentiments, and opinions within online reviews. Marketers can leverage these insights to inform the development of culturally sensitive strategies, enhance target audience segmentation, and refine messaging approaches aligned with cultural preferences. Anchored in Hofstede's Cultural Dimensions theory, our research employs sophisticated methodologies to delve into the intricate relationship between cultural differences and online consumer behaviors. Applied to specific cultural dimensions, such as individualism vs. collectivism, masculinity vs. femininity, uncertainty avoidance, and long-term vs. short-term orientation, the study uncovers nuanced insights. For example, in exploring individualism vs. collectivism, we examine how reviewers from individualistic cultures prioritize personal experiences while those from collectivistic cultures emphasize communal opinions. Similarly, within masculinity vs. femininity, we investigate whether distinct topics align with cultural notions, such as robust features in masculine cultures and user-friendliness in feminine cultures. Examining information-seeking behaviors under uncertainty avoidance reveals how cultures differ in seeking detailed information or providing succinct reviews based on their comfort with ambiguity. Additionally, in assessing long-term vs. short-term orientation, the research explores how cultural focus on enduring benefits or immediate gratification influences reviews. These concrete examples contribute to the theoretical enhancement of Hofstede's Cultural Dimensions theory, providing a detailed understanding of cultural impacts on online consumer behaviors. As online reviews become increasingly crucial in decision-making, this research not only contributes to the academic understanding of cultural influences but also proposes practical recommendations for enhancing online review systems. Marketers can leverage these findings to design targeted and culturally relevant strategies, ultimately enhancing their global marketing effectiveness and optimizing online review systems for maximum impact. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title="comparative analysis">comparative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20dimensions" title=" cultural dimensions"> cultural dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=marketing%20intelligence" title=" marketing intelligence"> marketing intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=national%20culture" title=" national culture"> national culture</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20consumer%20behavior" title=" online consumer behavior"> online consumer behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/182123/cultural-dynamics-in-online-consumer-behavior-exploring-cross-country-variances-in-review-influence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5323</span> Understanding the Influence on Drivers’ Recommendation and Review-Writing Behavior in the P2P Taxi Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Liwen%20Hou">Liwen Hou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The booming mobile business has been penetrating the taxi industry worldwide with P2P (peer to peer) taxi services, as an emerging business model, transforming the industry. Parallel with other mobile businesses, member recommendations and online reviews are believed to be very effective with regard to acquiring new users for P2P taxi services. Based on an empirical dataset of the taxi industry in China, this study aims to reveal which factors influence users’ recommendations and review-writing behaviors. Differing from the existing literature, this paper takes the taxi driver’s perspective into consideration and hence selects a group of variables related to the drivers. We built two models to reflect the factors that influence the number of recommendations and reviews posted on the platform (i.e., the app). Our models show that all factors, except the driver’s score, significantly influence the recommendation behavior. Likewise, only one factor, passengers’ bad reviews, is insignificant in generating more drivers’ reviews. In the conclusion, we summarize the findings and limitations of the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20recommendation" title="online recommendation">online recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=P2P%20taxi%20service" title=" P2P taxi service"> P2P taxi service</a>, <a href="https://publications.waset.org/abstracts/search?q=review-writing" title=" review-writing"> review-writing</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20of%20mouth" title=" word of mouth"> word of mouth</a> </p> <a href="https://publications.waset.org/abstracts/49579/understanding-the-influence-on-drivers-recommendation-and-review-writing-behavior-in-the-p2p-taxi-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49579.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5322</span> A Recommender System Fusing Collaborative Filtering and User’s Review Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seulbi%20Choi">Seulbi Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Recommender%20system" title="Recommender system">Recommender system</a>, <a href="https://publications.waset.org/abstracts/search?q=Collaborative%20filtering" title=" Collaborative filtering"> Collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=Text%20mining" title=" Text mining"> Text mining</a>, <a href="https://publications.waset.org/abstracts/search?q=Review%20mining" title=" Review mining"> Review mining</a> </p> <a href="https://publications.waset.org/abstracts/54867/a-recommender-system-fusing-collaborative-filtering-and-users-review-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">356</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5321</span> Internet Shopping: A Study Based On Hedonic Value and Flow Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pui-Lai%20To">Pui-Lai To</a>, <a href="https://publications.waset.org/abstracts/search?q=E-Ping%20Sung"> E-Ping Sung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the flourishing development of online shopping, an increasing number of customers see online shopping as an entertaining experience. Because the online consumer has a double identity as a shopper and an Internet user, online shopping should offer hedonic values of shopping and Internet usage. The purpose of this study is to investigate hedonic online shopping motivations from the perspectives of traditional hedonic value and flow theory. The study adopted a focus group interview method, including two online and two offline interviews. Four focus groups of shoppers consisted of online professionals, online college students, offline professionals and offline college students. The results of the study indicate that traditional hedonic values and dimensions of flow theory exist in the online shopping environment. The study indicated that online shoppers seem to appreciate being able to learn things and grow to become competitive achievers online. Comparisons of online hedonic motivations between groups are conducted. This study serves as a basis for the future growth of Internet marketing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flow%20theory" title="flow theory">flow theory</a>, <a href="https://publications.waset.org/abstracts/search?q=hedonic%20motivation" title=" hedonic motivation"> hedonic motivation</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20shopping" title=" internet shopping"> internet shopping</a> </p> <a href="https://publications.waset.org/abstracts/29860/internet-shopping-a-study-based-on-hedonic-value-and-flow-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29860.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5320</span> Increasing Student Engagement in Online Educational Leadership Courses</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mark%20Deschaine">Mark Deschaine</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Whale"> David Whale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Utilization of online instruction continues to increase at universities, placing more emphasis on the exploration of issues related to adult graduate student engagement. This reflective case study reviews non-traditional student engagement in online courses. The goals of the study are to enhance student focus, attention and interaction. Findings suggest that interactivity seemed to be a key in keeping students involved and achieving, with specific activities routinely favored by students. It is recommended that time spent engaging students is worthwhile and results in greater course satisfaction and academic effort. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20learning" title="online learning">online learning</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20achievement" title=" student achievement"> student achievement</a>, <a href="https://publications.waset.org/abstracts/search?q=student%20engagement" title=" student engagement"> student engagement</a>, <a href="https://publications.waset.org/abstracts/search?q=technology" title=" technology"> technology</a> </p> <a href="https://publications.waset.org/abstracts/67208/increasing-student-engagement-in-online-educational-leadership-courses" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67208.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5319</span> Analyzing Behaviour of the Utilization of the Online News Clipping Database: Experience in Suan Sunandha Rajabhat University</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siriporn%20Poolsuwan">Siriporn Poolsuwan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kanyarat%20Bussaban"> Kanyarat Bussaban</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to investigate and analyze user’s behaviour towards the utilization of the online news clipping database at Suan Sunandha Rajabhat University, Thailand. Data is gathered from 214 lecturers and 380 undergraduate students by using questionnaires. Findings show that most users knew the online news clipping service from their friends, library’s website and their teachers. The users learned how to use it by themselves and others learned by training of SSRU library. Most users used the online news clipping database one time per month at home and always used the service for general knowledge, up-to-date academic knowledge and assignment reference. Moreover, the results of using the online news clipping service problems include the users themselves, service management, service device- computer and tools – and the network, service provider, and publicity. This research would be benefit for librarians and teachers for planning and designing library services in their works and organization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20database" title="online database">online database</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20behavior" title=" user behavior"> user behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=news%20clipping" title=" news clipping"> news clipping</a>, <a href="https://publications.waset.org/abstracts/search?q=library%20services" title=" library services"> library services</a> </p> <a href="https://publications.waset.org/abstracts/8176/analyzing-behaviour-of-the-utilization-of-the-online-news-clipping-database-experience-in-suan-sunandha-rajabhat-university" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8176.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">314</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5318</span> Online Educational Tools and Language Teaching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petr%20Sulc">Petr Sulc</a>, <a href="https://publications.waset.org/abstracts/search?q=Hana%20Maresova"> Hana Maresova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This contribution focuses on online educational tools and language teaching, specifically on literary education in a multi-user virtual environment. The goal of this contribution is to give a basic overview of online language education and teaching in a virtual environment. The main goal of the research survey is to compare language (literary) education in a virtual environment with the traditional way of teaching in a typical classroom. The research concept will be mixed: a didactic test, the grounded theory method, and semi-structured questioning will be used. Kitely’s multi-user virtual environment and printed worksheets will be used for the comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20educational%20tools" title="online educational tools">online educational tools</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20environment" title=" virtual environment"> virtual environment</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20teaching%20objects" title=" virtual teaching objects"> virtual teaching objects</a>, <a href="https://publications.waset.org/abstracts/search?q=literary%20education" title=" literary education"> literary education</a>, <a href="https://publications.waset.org/abstracts/search?q=didactic%20test" title=" didactic test"> didactic test</a> </p> <a href="https://publications.waset.org/abstracts/142264/online-educational-tools-and-language-teaching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5317</span> Feature-Based Summarizing and Ranking from Customer Reviews</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dim%20En%20Nyaung">Dim En Nyaung</a>, <a href="https://publications.waset.org/abstracts/search?q=Thin%20Lai%20Lai%20Thein"> Thin Lai Lai Thein</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the rapid increase of Internet, web opinion sources dynamically emerge which is useful for both potential customers and product manufacturers for prediction and decision purposes. These are the user generated contents written in natural languages and are unstructured-free-texts scheme. Therefore, opinion mining techniques become popular to automatically process customer reviews for extracting product features and user opinions expressed over them. Since customer reviews may contain both opinionated and factual sentences, a supervised machine learning technique applies for subjectivity classification to improve the mining performance. In this paper, we dedicate our work is the task of opinion summarization. Therefore, product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the identification of semantic relationships. The polarity and numeric score of all the features are determined by Senti-WordNet Lexicon. The problem of opinion summarization refers how to relate the opinion words with respect to a certain feature. Probabilistic based model of supervised learning will improve the result that is more flexible and effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=opinion%20mining" title="opinion mining">opinion mining</a>, <a href="https://publications.waset.org/abstracts/search?q=opinion%20summarization" title=" opinion summarization"> opinion summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title=" sentiment analysis"> sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining" title=" text mining"> text mining</a> </p> <a href="https://publications.waset.org/abstracts/25595/feature-based-summarizing-and-ranking-from-customer-reviews" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25595.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5316</span> Emerging Methods as a Tool for Obtaining Subconscious Feedback in E-Commerce and Marketplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Ber%C4%8D%C3%ADk">J. Berčík</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mravcov%C3%A1"> A. Mravcová</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ruskov%C3%A1"> A. Rusková</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Jur%C4%8Di%C5%A1in"> P. Jurčišin</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Vir%C3%A1gh"> R. Virágh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The online world is changing every day. With this comes the emergence and development of new business models. One of them is the sale of several types of products in one place. This type of sales in the form of online marketplaces has undergone a positive development in recent years and represents a kind of alternative to brick-and-mortar shopping centres. The main philosophy is to buy several products under one roof. Examples of popular e-commerce marketplaces are Amazon, eBay, and Allegro. Their share of total e-commerce turnover is expected to even double in the coming years. The paper highlights possibilities for testing web applications and online marketplace using emerging methods like stationary eye cameras (eye tracking) and facial analysis (FaceReading). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emerging%20methods" title="emerging methods">emerging methods</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20neuroscience" title=" consumer neuroscience"> consumer neuroscience</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=marketplace" title=" marketplace"> marketplace</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20experience" title=" user experience"> user experience</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20interface" title=" user interface"> user interface</a> </p> <a href="https://publications.waset.org/abstracts/170651/emerging-methods-as-a-tool-for-obtaining-subconscious-feedback-in-e-commerce-and-marketplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5315</span> Review for Identifying Online Opinion Leaders</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu%20Wang">Yu Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20opinion%20leaders" title="online opinion leaders">online opinion leaders</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20attributes%20analysis" title=" user attributes analysis"> user attributes analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20mining%20analysis" title=" text mining analysis"> text mining analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20structure%20analysis" title=" network structure analysis"> network structure analysis</a> </p> <a href="https://publications.waset.org/abstracts/75740/review-for-identifying-online-opinion-leaders" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75740.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5314</span> Defining Affecting Factors on Rate of Car E-Customers' Satisfaction – a Case Study of Iran Khodro Co.</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Mohammadi">Majid Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Yosef%20Zadeh"> Mohammad Yosef Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vahid%20Naderi%20Darshori"> Vahid Naderi Darshori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this research is concreting of satisfaction literature for obtain index with online content in carmaker industry. The study measures customer satisfaction of online and collect from similar studies with reference to a model of online satisfaction, they are attempting to complete. Statistical communities of research are online customers' carmaker Iran Khodro has been buying the company's products in the last six months. One of the innovative measures in this study is that, customer reviews are obtained through an Internet site. Reliability of the data collected in this study, the Cronbach's alpha coefficient was approved. The coefficient of 0.828 was calculated for the questionnaire. To test the hypothesis, the Pearson correlation coefficient was used. To ensure the correctness of initial theoretical model, we used regression analyzes and structural equation weight and finally, the results obtained with little change to the basic model of research, are improved and completed. At last obtain the perceived value has most direct effect on online car customers satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=customer%20satisfaction" title="customer satisfaction">customer satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20satisfaction" title=" online satisfaction"> online satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20customer" title=" online customer"> online customer</a>, <a href="https://publications.waset.org/abstracts/search?q=car" title=" car"> car</a> </p> <a href="https://publications.waset.org/abstracts/27945/defining-affecting-factors-on-rate-of-car-e-customers-satisfaction-a-case-study-of-iran-khodro-co" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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