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نتیجه جستجو - Product reviews

تعداد مقالات یافته شده: 12
ردیف عنوان نوع
1 An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews
ارزیابی نظرات و برداشت از ترموستات هوشمند با استفاده از تحلیل احساسات مبتنی بر جنبه های بررسی های آنلاین-2020
Smart thermostats have been on the market for nearly a decade, with an estimated adoption rate of 7% in 2018. With many regions of the U.S. having a heating and/or cooling system in nearly 100% of households, there is significant opportunity for further adoption, which can help support energy savings and building-grid interactions. However, more insight is needed to provide a better understanding of their utilization and user opinions. In this study, online reviews are used to evaluate users’ perceptions and attitudes towards smart thermostats. 26,372 product reviews were collected for five commercially-available smart thermostats and were analyzed with a confirmatory aspect-based opinion mining technique. An analysis of this dataset shows that the characteristics of the current user population show substantial differences compared to the more widely studied early adopters. When comparing the most commonly discussed topics, users generally do not discuss the energy and cost savings related features of their devices in comparison to other topics such as control, ease of use, and installation. In addition, comfort is discussed nearly twice as much as energy efficiency. The results of this work can help product manufacturers and utility providers to push towards more widespread adoption and efficient use.
Keywords: Energy savings | Smart thermostats | Smart home technology | Home energy management | Opinion mining
مقاله انگلیسی
2 User perception of sentiment-integrated critiquing in recommender systems
درک کاربر از نقد احساساتی در سیستم های توصیه گر-2019
Critiquing in recommender systems has been accepted as an effective feedback mechanism that allows users to incrementally refine their preferences for product attributes, especially in complex decision environments and high-investment product domains where users ’initial preferences are usually uncertain and incomplete. However, the traditional critiquing methods are limited in that they are only based on static attribute values (such as a digital camera ’s screen size, effectiveness pixels, optical zoom). Considering product reviews contain other customers ’sentiments (also called opinions) expressed on some features, in this manuscript, we propose a sentiment-integrated critiquing approach, for helping users to formulate and refine their preferences. Through both before-after and within-subjects experiments, we find that the incorporation of feature sentiments into the critiquing interface can significantly improve users ’product knowledge, preference certainty, decision confidence, perceived information usefulness, and purchase intention. The results can hence be constructive for enhancing current critiquing-based recommender systems.
Keywords: Critiquing-based recommender systems | Product reviews | Feature-based sentiment analysis | E-commerce | User evaluation
مقاله انگلیسی
3 Implicit aspect extraction in sentiment analysis: Review, taxonomy, oppportunities, and open challenges
استخراج ضمنی جنبه در تحلیل احساسات: مرور، طبقه بندی، فرصت ها و چالش های باز-2018
Sentiment analysis is a text classification branch, which is defined as the process of extracting sentiment terms (i.e. feature/aspect, or opinion) and determining their opinion semantic orientation. At aspect level, aspect extraction is the core task for sentiment analysis which can either be implicit or explicit aspects. The growth of sentiment analysis has resulted in the emergence of various techniques for both explicit and implicit aspect extraction. However, majority of the research attempts targeted explicit aspect extraction, which indicates that there is a lack of research on implicit aspect extraction. This research provides a review of implicit aspect/features extraction techniques from different perspectives. The first perspective is making a comparison analysis for the techniques available for implicit term extraction with a brief summary of each technique. The second perspective is classifying and comparing the performance, datasets, language used, and shortcomings of the available techniques. In this study, over 50 articles have been reviewed, however, only 45 articles on implicit aspect extraction that span from 2005 to 2016 were analyzed and discussed. Majority of the researchers on implicit aspects extraction rely heavily on unsupervised methods in their research, which makes about 64% of the 45 articles, followed by supervised methods of about 27%, and lastly semi-supervised of 9%. In addition, 25 articles conducted the research work solely on product reviews, and 5 articles conducted their research work using product reviews jointly with other types of data, which makes product review datasets the most frequently used data type compared to other types. Furthermore, research on implicit aspect features extraction has focused on English and Chinese languages compared to other languages. Finally, this review also provides recommendations for future research directions and open problems.
keywords: Aspect extraction| Implicit aspect| Implicit feature| Sentiment analysis| Sentiment extraction
مقاله انگلیسی
4 Aspect ontology based review exploration
هستی شناسی جنبه برمبنای بررسی و مرور-2018
User feedback in the form of customer reviews, blogs, and forum posts is an essential feature of e-commerce. Users often read online product reviews to get an insight into the quality of various aspects of a product. Besides, users have different aspect preferences, and they look for reviews that contain relevant information regarding their preferred aspect(s). However, as reviews are unstructured and voluminous, it becomes exhaustive and laborious for users to find relevant reviews. Lack of domain knowledge about various aspects and sub-aspects of a product, and how they are related to each other, also add to the problem. Although this information could be there in product reviews, it is not easy for users to spot it instantly from the reviews. This paper seeks to address the above problems and presents two novel algorithms that summarize product reviews, and provides an interactive search interface, similar to popular faceted navigation. We solve the problem by creating an aspect ontology tree with high aspect extraction precision.
keywords: Electronic commerce |Review exploration |Opinion mining |Aspect ontology
مقاله انگلیسی
5 Measuring design-level information quality in online reviews
اندازه گیری کیفیت اطلاعات سطح - طراحی در بازبینی های آنلاین-2018
Online product reviews are an important type of user-generated content. For product designers, they offer valuable information that identifies consumer likes, dislikes and desires. We investigate the volume and quality of product design information available in online reviews and introduce the design-level information quality (DLIQ) measure. DLIQ is indicative of the design contextual information stored in the online reviews for a given product. Three separate information components are evaluated: content, complexity, and relevancy. Key determinants of DLIQ are the number of reviews, sentences, words, noun words and feature matching noun words in a review database. DLIQ is formulated as an index and indicates information content relative to a sample of products. For a sample of ten products, RapidMiner was used to mine and illustrate the DLIQ. A validation test with a survey group confirms DLIQ is a reliable predictor of actionable design information in the review database.
keywords: Big data |Data analytics |Design features |Information quality |Opinion mining |Online reviews |Product design
مقاله انگلیسی
6 Ranking online consumer reviews
رتبه بندی بازدیدهای آنلاین مشتری-2018
Product reviews are posted online by the hundreds and thousands for popular products. Handling such a large volume of continuously generated online content is a challenging task for buyers, sellers and researchers. The purpose of this study is to rank the overwhelming number of reviews using their predicted helpfulness scores. The helpfulness score is predicted using features extracted from review text, product description, and customer question-answer data of a product using the random-forest classifier and gradient boosting regressor. The system classifies reviews into low or high quality with the random-forest classifier. The helpfulness scores of the high-quality reviews are only predicted using the gradient boosting regressor. The helpfulness scores of the low-quality reviews are not calculated because they are never going to be in the top k reviews. They are just added at the end of the review list to the review-listing website. The proposed system provides fair review placement on review listing pages and makes all high-quality reviews visible to customers on the top. The experimental results on data from two popular Indian e-commerce websites validate our claim, as 3–4 newer high-quality reviews are placed in the top ten reviews along with 5–6 older reviews based on review helpfulness. Our findings indicate that inclusion of features from product description data and customer question-answer data improves the prediction accuracy of the helpfulness score.
keywords: Big data challenge |E-commerce |Helpfulness |Machine learning |Online reviews
مقاله انگلیسی
7 Topic analysis of online reviews for two competitive products using latent Dirichlet allocation
تحلیل موضوعی بازدیدهای آنلاین برای دو محصول رقابتی با استفاده از تخصیص نهان دیریکله-2018
The voice of the customer plays an important role in product competition. Traditional methods in the area have largely focused on market research and questionnaire surveys to obtain customer preferences. However, online product reviews have provided a good and reliable channel for not only understanding customers needs for one product or service but also analyzing products’ competition in the market. In this paper, we propose a new framework of applying online product reviews to analyze customer preferences for two competitive products. We extract the key topics of online reviews for two specific competitive products via a text mining approach of latent Dirichlet allocation (LDA). Topic difference analysis demonstrates the unique topics of the two products. The relative importance and topic heterogeneity analyses identify the competitive superiorities and weaknesses of both products. Two case studies that are presented demonstrate the efficacy of the proposed framework. The method also provides valuable managerial implications for product designers and e-commerce companies.
keywords: Competitive analysis |Latent Dirichlet allocation |Online product reviews |Product competition |Text mining |Topic analysis
مقاله انگلیسی
8 Predicting the helpfulness of online product reviews: A multilingual approach
پیش بینی مفید بودن بازدیدهای آنلاین از محصول: یک دیدگاه چند زبانه-2018
Identifying helpful reviews from massive review data has been a hot topic in the past decade. While existing research on review helpfulness estimation and prediction is primarily sourced from English reviews, non-English reviews may also provide useful consumer opinion information and should not be neglected. In this study, we propose a review helpfulness prediction framework that processes and uses multilingual sources of reviews to generate relevant business insights. Adopting a design science research approach, we design, implement, evaluate and deliver an IT artifact (i.e., our framework) that predicts the helpfulness of a review and accounts for non-English reviews. Our evaluations suggest that we achieve better performance on review helpfulness prediction and classification by including the variables generated by our instantiated multilingual system. By demonstrating the feasibility of our proposed framework for multilingual business intelligence applications, we contribute to the literature on business intelligence and provide important practical implications to practitioners.
keywords: Word-of-mouth |Product reviews |Multilingual reviews |Review helpfulness |Prediction
مقاله انگلیسی
9 The Opinion Management Framework: Identifying and addressing customer concerns extracted from online product reviews
چارچوب مدیریت نظرات: شناسایی و بررسی نگرانی های استخراج شده مشتری از بازدیدهای آنلاین از محصول-2018
Online product reviews appear in many e-commerce websites and help merchants understand any obstacles experienced by existing customers. Negative reviews can discourage potential customers, especially when such reviews appear with no response from the merchant. After the appearance of an unfavourable review, the merchant is at risk of incurring negative impact on the community of present and future customers, which can harm the business. He or she may be able to deflect this by promptly communicating any planned actions, completing them, and reporting that they are complete. The initial communication is the most urgent. When presented with a set of online reviews, a merchant’s predicament is to quickly decide what tasks need to be done, which are the most important, and when each can be completed. In this paper, we describe our Opinion Management Framework that assists a merchant to quickly identify, select, and schedule tasks that can rectify issues mentioned in online reviews. We also describe an interactive web-based prototype that helps the business owner (1) to select a set of tasks with an optimal cost/benefit tradeoff, (2) to ensure that all tasks can be completed within a specific time limit, and (3) to conservatively estimate a completion date for each issue’s resolution.
keywords: Social commerce |Topic extraction |Opinion extraction |Actionable analytics |Project management |Optimal task selection |Critical path |Electronic word of mouth
مقاله انگلیسی
10 The evolution of sentiment analysis—A review of research topics, venues, and top cited papers
تحول تحليل احساسات - بازبينی موضوعات پژوهشی،سالنها، و مقالات فوق الذکر-2018
Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis.
Keywords: Sentiment analysis ، Opinion mining ، Bibliometric study ، Text mining ، Literature review ، Topic modeling ، Latent Dirichlet Allocation ، Qualitative analysis
مقاله انگلیسی
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