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《Information processing & management》2020,57(2):102179
Online review helpfulness has always sparked a heated discussion among academics and practitioners. Despite the fact that research has extensively examined the impacts of review title and content on perceptions of online review helpfulness, the underlying mechanism of how the similarities between a review' title and content may affect review helpfulness has been rarely explored. Based on mere exposure theory, a research model reflecting the influences of title-content similarity and sentiment consistency on review helpfulness was developed and empirically examined by using data collected from 127,547 product reviews on Amazon.com. The TF-IDF and the cosine of similarity were used for measuring the text similarity between review title and review content, and the Tobit model was used for regression analysis. The results showed that the title-content similarity positively affected review helpfulness. In addition, the positive effect of title-content similarity on review helpfulness is increased when the title-content sentiment consistency is high. The title sentiment also negatively moderates the impact of the title-content similarity on review helpfulness. The present research can help online retailers identify the most helpful reviews and, thus, reduce consumers' search costs as well as assist reviewers in contributing more valuable online reviews. 相似文献
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电子商务中在线评论有用投票数影响因素研究 总被引：1，自引：0，他引：1
在线评论对消费者购物选择具有重要的影响,但日益增加的海量信息导致了信息过载等问题。因此,判断和识别评论信息的有用性具有重要的研究意义。本文采用文本挖掘和统计分析方法,从评论信息特征和评论者信息两个角度,对在线评论获得有用投票数的影响因素进行了分析,并通过亚马逊商城的用户评论样本,具体研究了各因素的影响作用。研究发现：评论评分对在线评论的有用投票数具有负向影响,而评论信息丰富性和历史评论有用性评价对其具有正向影响。 相似文献
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商品在线评论有用性——基于品牌的调节作用分析 总被引：1，自引：0，他引：1
商品的在线评论已经成为影响顾客进行在线购买的重要因素。本文以亚马逊（中国）网站为例,研究了在线评论、评论者的数字特征对评论有用性的影响。通过对五种在线商品的1 845条在线评论进行实证分析,研究发现：在线评论的评论长度和评论者排名对评论有用性具有正向影响,同时评论长度对有用性的影响受到品牌的调节作用。另外,评论星级对评论有用性的影响也受到品牌的调节作用,并且对于大品牌产品而言,评论的极端性与评论有用性呈"U"形关系。 相似文献
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《Information processing & management》2019,56(4):1425-1438
This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review. 相似文献
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Web2.0时代,阅读在线产品评论已经成为人们购物前的一种习惯。然而,网络上的评论数量巨大且观点不一,消费者很难获取到真正对其有用的评论。本文从研究中文在线产品评论的有用性评估入手,结合中文在线评论的特点,构建了评论有用性评估特征体系。以二分类思想为中心,基于文本挖掘的基本流程,实现对中文产品评论的分类,并考察了评论内容各特征对分类效果的影响。结果表明,本文提出的评估方法能有效识别出有用评论,并且发现浅层句法特征在分类中的贡献度较高,语义特征与情感特征则会因语料类型的不同而有不同的分类贡献度。 相似文献
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[目的/意义]在线评论在消费者网络购物决策过程中解决信息不对称的作用日益显著,探索在线评论有用性影响因素对消费者和商家都具有重要意义。[方法/过程]以信号传递理论为框架,从与评论内容、评论者和反馈有关的信号构建在线评论有用性影响因素模型,同时考虑商品类型的调节作用,并分析了信号环境的影响。[结果/结论]通过亚马逊中国网站获取客观数据进行实证研究,发现负面评论、评论字数越多、评论含有图片、评论者对信息有披露、评论者排名越靠前、评论回应数量越多则评论有用性越高,商品类型在评论情感倾向、评论图片对评论有用性影响中起到了显著的调节作用,并且信号影响评论有用性受到信号环境的影响。 相似文献
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《Information processing & management》2020,57(5):102221
Although statistical learning methods have achieved success in e-commerce platform product review sentiment classification, two problems have limited its practical application: 1) The computational efficiency to process large-scale reviews; 2) the ability to continuously learn from increasing reviews and multiple domains. This paper presents a continuous naïve Bayes learning framework for large-scale and multi-domain e-commerce platform product review sentiment classification. While keeping the high computational efficiency of the traditional naïve Bayes model, we extend the parameter estimation mechanism in naïve Bayes to a continuous learning style. We furthermore propose ways to fine-tune the learned distribution based on three kinds of assumptions to better adapt to different domains. Experimental results on the Amazon product and movie review sentiment datasets show that our model can use the knowledge learned from past domains to guide learning in new domains, and has a better capacity of dealing with reviews that are continuously updated and come from different domains. 相似文献
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本研究以ELM模型为研究框架,从用户处理评论信息过程的视角,探究对于不同属性的商品,用户感知评论有用性的行为和结果是否存在差异。利用Amazon.com上的5 420条有效评论样本进行实验,研究发现:商品类型会影响用户感知在线评论有用性的方式,进而对在线评论有用性产生调节影响;用户更偏向通过中心路径来感知搜索类商品评论信息有用性大小,而在感知体验类商品评论信息有用性时,需结合中心和边缘两条路径。 相似文献
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随着互联网的迅速发展,消费者购物决策越来越依赖于在线评论。从信息接受模型对信息有用性影响出发,构建在线评论有用性投票增量的时间窗,建立在线评论有用性影响因素模型,研究在线评论内容和评论者对在线评论有用性的影响。基于TripAdvisor.com的4 258条酒店评论数据,运用负二项回归进行实证分析。研究发现评论内容长度、评论极端性、评论有用性投票数、评论者认可度和个人信息披露对在线评论有用性具有显著正影响,这对在线零售商和消费者具有重要的启发和建议。 相似文献
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《Information processing & management》2023,60(2):103201
A growing number of enterprises begin to utilize user-generated content (UGC) to help build brand awareness and loyalty on social media platforms. Thus, it is important to investigate what makes UGC more helpful under the new social media environment. This study attempts to identify the influence mechanism of UGC helpfulness by examining the systematic impacts of argument quality and source reliability, especially considering the effects of creator interactivity. Using a dataset of product-related UGC in a popular social media app, our empirical study finds that the detailedness, readability, and objectivity of the content, as well as the social recognition and popularity of the creator, all have a significant impact on UGC helpfulness. Furthermore, the results indicate that creator interactivity plays a vital role in building UGC helpfulness by moderating other factors. This study contributes to both UGC and social media literature by proposing a comprehensive model to better understand UGC helpfulness. It also provides several practical insights for content creators to improve their online performances. 相似文献
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Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification. 相似文献
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《Information processing & management》2022,59(5):103028
Assigning paper to suitable reviewers is of great significance to ensure the accuracy and fairness of peer review results. In the past three decades, many researchers have made a wealth of achievements on the reviewer assignment problem (RAP). In this survey, we provide a comprehensive review of the primary research achievements on reviewer assignment algorithm from 1992 to 2022. Specially, this survey first discusses the background and necessity of automatic reviewer assignment, and then systematically summarize the existing research work from three aspects, i.e., construction of candidate reviewer database, computation of matching degree between reviewers and papers, and reviewer assignment optimization algorithm, with objective comments on the advantages and disadvantages of the current algorithms. Afterwards, the evaluation metrics and datasets of reviewer assignment algorithm are summarized. To conclude, we prospect the potential research directions of RAP. Since there are few comprehensive survey papers on reviewer assignment algorithm in the past ten years, this survey can serve as a valuable reference for the related researchers and peer review organizers. 相似文献
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《International Journal of Information Management》2017,37(3):202-213
Due to the proliferation of Web 2.0 technology, e-commerce has evolved into social commerce. In this social commerce era, consumers are increasingly dependent on each other and look for social support (informational and emotional) online even before making purchases. This study examines the content of consumer reviews, a fundamental construct of social commerce. Topics expressed in consumer reviews (collected from Amazon.com) are explored using a machine learning technique (i.e. latent semantic analysis). This study documents the thematic differences between positive and negative reviews and finds that negative reviews report service-related failures while positive reviews relate more to the product, among other things. Next, the informational support aspect of social commerce is explored by identifying the topics expressed in reviews that are helpful in purchase decisions. The findings demonstrate that potential customers (i.e. those who would like to purchase a product in the near future and currently are reading reviews with the intention to decide whether or not to buy that product) find the negative reviews containing service failure information and the positive reviews containing information on core functionalities, technical aspects, and aesthetics to be more helpful. Theoretical and managerial implications are discussed. 相似文献
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With the presence of fake reviews on e-commerce platforms, the reliability of reviews becomes questionable. The extant literature demonstrates the impact of fake reviews on product sales and proposes several algorithms to prevent fake reviews from being displayed on the platform. However, what has largely remained uninvestigated is how customers perceive reviews present on the e-commerce platform. Based on the speech act theory, we develop a theoretical framework that explains how the linguistic style (both at the word and the structural level) acts as a cue for assessing a reviewer’s (in)sincere intentions. We evaluate the framework on a corpus of 120 online product reviews – each examined by at least 50 customers – using the fractional logit model. Results suggest that the communication style of a speaker reflects his/her intention. Reviews with less contextual embedding, argument structuring, and flattering through non-verbal cues trigger customers towards perceiving a review as deceptive. 相似文献
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Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy. 相似文献
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个性化的旅游服务往往能得到客户更多的关注。潜在客户对旅行社计划推出的旅游线路的关注信息,常出现在留言板上或者关于旅游线路或景点的投票页上,利用这些信息,借助文本挖掘和机器学习技术,可以找出最适合推荐给潜在客户的旅游线路,从而实现旅游服务（线路）的推介。 相似文献
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【目的】对影响开放式同行评议实践的相关因素进行实证研究,发掘开放式同行评议的关键影响因素。【方法】以Directory of Open Access Journals (DOAJ)中开放式同行评议期刊为研究对象,通过网络爬取相关数据。采用变量分类赋值的方式,对影响开放式同行评议的相关定性因素进行量化分析。采用多重对应分析图展示开放式同行评议相关影响因素及其不同类别的内在关联;采用最优尺度回归模型揭示相关影响因素对开放评议类型的影响程度。【结果】开放评议类型与评议专家身份的公开类别具有极密切的关联,评议专家身份对开放评议类型有显著正向影响,且重要性程度值非常高。【结论】评议专家身份是否公开成为开放式同行评议实践模式的关键影响因素,透明性同行评议是当前开放评议行之有效的实践模式。 相似文献