消费者在线评论有用性影响因素模型研究

消费者在线评论的价值已经得到消费者和在线零售商的公认,对评论有用性的研究已经成为新的研究热点。从减少消费者决策风险出发,在感知诊断性概念基础上定义了评论有用性概念,构建了一个评论有用性影响因素模型。从传播说服理论的维度考察,评论等级、评论长度、好评率和使用互联网经验是影响评论有用性的重要因素。商品类型对评论有用性具有调节作用。     

Can online user reviews be more helpful? Evaluating and improving ranking approaches

Given the sharply increasing number of online reviews, the selection of strategies by review-hosting firms to help users access more helpful reviews is an intriguing but insufficiently studied issue. We first propose a model to help us understand how reviews receive helpful votes (HV) and non-helpful votes. According to this model, the performances of different ranking approaches are compared using several simulated datasets with empirical features. In addition to three well-known ranking approaches, we develop a novel approach based on Bayesian statistics that is easy to implement in existing websites and can be combined with other content recommendation techniques to determine the prior belief in online reviews. More importantly, we suggest two simple ways to enhance existing ranking approaches. The numerical evidence demonstrates the advantages of two enhanced approaches, as indicated by higher helpful ratios and a reduced Matthew effect. These findings have important practical implications for consumers, online retailers, and review-hosting firms.     

A study of factors that contribute to online review helpfulness

Helpfulness of online reviews is a multi-faceted concept that can be driven by several types of factors. This study was designed to extend existing research on online review helpfulness by looking at not just the quantitative factors (such as word count), but also qualitative aspects of reviewers (including reviewer experience, reviewer impact, reviewer cumulative helpfulness). This integrated view uncovers some insights that were not available before. Our findings suggest that word count has a threshold in its effects on review helpfulness. Beyond this threshold, its effect diminishes significantly or becomes near non-existent. Reviewer experience and their impact were not statistically significant predictors of helpfulness, but past helpfulness records tended to predict future helpfulness ratings. Review framing was also a strong predictor of helpfulness. As a result, characteristics of reviewers and review messages have a varying degree of impact on review helpfulness. Theoretical and practical implications are discussed.     

An examination of the celebrity endorsements and online customer reviews influence female consumers’ shopping behavior

The goal of this study is to compare the influence of celebrity endorsements to online customer reviews on female shopping behavior. Based on AIDMA and AISAS models, we design an experiment to investigate consumer responses to search good and experience good respectively. The results revealed that search good (shoes) endorsed by a celebrity in an advertisement evoked significantly more attention, desire, and action from the consumer than did an online customer review. We also found that online customer reviews emerged higher than the celebrity endorsement on the scale of participants’ memory, search and share attitudes toward the experience good (toner). Implications for marketers as well as suggestions for future research are discussed.     

Online review manipulation by asymmetrical firms: Is a firm’s manipulation of online reviews always detrimental to its competitor?

Online reviews have a significant influence on consumers, and consequently firms are motivated to manipulate online reviews to promote their own products. This paper develops an analytical model to systematically explore the impact of online review manipulation on asymmetrical firms who sell substitutable search products in a competing market. Results show that a firm’s manipulation of online reviews is not necessary to hurt its competitor’s profit. In addition, if firms are free to choose whether to manipulate online reviews, both firms will always choose to manipulate online reviews. Moreover, there exists a prisoner’s dilemma in which online reviews are overmanipulated.     

专业化音乐教学信息搜索引擎技术研究

随着音乐教学信息的激增,在网络上查找精确音乐信息变得越来越困难,专业化引擎技术将改进现有的搜索引擎和系统,构建一个基于Web个性化音乐信息推荐服务的搜索引擎框架结构,满足用户日益增长的特定信息需求。     

Sources and impacts of social influence from online anonymous user reviews

Consumers increasingly rely on online reviews to make purchase decisions. However, the process through which consumers are influenced by online reviews is not well understood. To fill the gap, we apply the social influence theory to theoretically explain and analyze this opinion change process. Specifically, we identify antecedents and consequences of two types of social influence from online user reviews: informational and value-expressive influence. From a survey conducted in a controlled laboratory environment, we found that perceived review quality positively impacts informational influence, while perceived review quality, consistency, and social presence jointly impact value-expressive influence. Interestingly, informational influence impacts both perceived decision quality and perceived usefulness of the website, while value-expressive influence only impacts perceived usefulness of the website. Surprisingly, consumer characteristics, including prior product expertise and self-monitoring, do not have significant effects on the formation of social influence online.     

The effect of content depth and deviation on online review helpfulness: Evidence from double-hurdle model

How does the content of a product review shape its perceived value? We propose two information theory-based constructs derived from probabilistic topic models and show their relationship with review helpfulness. The first construct, content depth, quantifies the breadth-depth tradeoff of a review and has an informational influence on readers’ voting behavior. The second construct, content deviation, indicates the deviance of the review content in comparison with others and exerts a normative influence on readers’ voting behavior. Noting the possibility that a review can get voted but has zero helpfulness score, we use a double-hurdle model to simultaneously estimate the probability of a review being voted and its helpfulness. The analyses on three product categories show that reviews with more depth and less content deviation are rated more helpful. Further, the relationships are moderated by a number of factors, including the deviation of numerical rating, recency of the review, and the reputation of the reviewer. The research contributes to the literature by showing how the content of a review and the interaction of content and numerical ratings jointly create value for consumers.     

When Consistency Matters: The Effect of Valence Consistency on Review Helpfulness

When evaluating the helpfulness of online reviews, review valence is a particularly relevant factor. This research argues that the influence of review valence is highly dependent on its consistency with the valence of other available reviews. Using both field and experimental data, this paper show that consistent reviews are perceived as more helpful than inconsistent reviews, independent of them being positive or negative. Experiments show that this valence consistency effect is driven by causal attributions, such that consistent reviews are more likely to be attributed to the actual product experience, while inconsistent reviews are more likely to be attributed to some reviewer idiosyncrasy. Supporting the attribution theory framework, reviewer expertise moderates the effect of consumers' causal attributions on review helpfulness.     

中文网络评论的IT产品特征挖掘及情感倾向分析

为探索中文客户评论中的IT产品特征及相关情感倾向的挖掘,帮助IT生产商和服务商提高改进产品和服务质量,提高竞争力。该文将采用情感分析技术,提出基于客户感知价值的产品特征挖掘算法,实现对于评论中IT产品特征及其情感倾向的语义分析、动态提取和综合信息挖掘;并根据用户的关注权重将产品特征和情感倾向进行排列。采用从互联网下载的真实IT产品评论语料中进行实验,初步验证了该方法的有效性。     

利用三层条件随机场模型进行情感极性分类及强度分析

通过对商品评论进行基于方面的情感分析,可以得到某件商品各个方面的优劣情况。本文提出利用三层CRFs模型进行情感极性分类及强度分析。在CRFs模型中,融合了词、词性、语气词、程度词、方面和评价词的共现等特征。在情感句识别、情感极性分类和情感强度分析上得到的F1值分别为86.3%、77.2%、70.7%,证明了：(1)分层CRFs模型在各个层次的任务中都能取得较好的结果;(2) 语气词、程度词、方面和评价词的共现特征在情感分类时是的有效性。     

A concept-level approach to the analysis of online review helpfulness

Helpfulness of online reviews serves multiple needs of different Web users. Several types of factors can drive reviews' helpfulness. This study focuses on uninvestigated factors by looking at not just the quantitative factors (such as the number of concepts), but also qualitative aspects of reviewers (including review types such as the regular, comparative and suggestive reviews and reviewer helpfulness) and builds a conceptual model for helpfulness prediction. The set of 1500 reviews were randomly collected from TripAdvisor.com across multiple hotels for analysis. A set of four hypotheses were used to test the proposed model. Our results suggest that the number of concepts contained in a review, the average number of concepts per sentence, and the review type contribute to the perceived helpfulness of online reviews. The regular reviews were not statistically significant predictors of helpfulness. As a result, review types and concepts have a varying degree of impact on review helpfulness. The findings of this study can provide new insights to e-commerce retailers in understanding the importance of helpfulness of reviews.     

大容量专利文献网上检索系统的实现

该文结合大容量专利文献数据的特点,介绍了一种专利文献数据网上检索系统的结构和开发,并重点讲述了在数据库和检索软件中如何采用多种措施以提高系统检索速度。     

基于Agent的元搜索引擎系统研究

阐述基于代理的元搜索引擎的系统结构和的基本概念、存在的不足,介绍代理技术以及代理的基本结构.提出基于代理的元搜索引擎的体系结构.并详细介绍系统各个组成部分.     

搜索引擎技术简析

随着信息搜索日益成为互联网的主要应用．搜索引擎技术正成为计算机工业界和学术界争相研究和开发的热点。本文主要介绍搜索引擎的基本原理、工作过程及技术发展趋势．     

基于Web的全文搜索引擎的设计与实现

介绍了一个基于Web的全文搜索引擎的设计及其实现,并对该搜索引擎的基本框架设计和主要功能模块的实现作了详细介绍,最后给出了相应的试验结果。     

搜索引擎技术简析

随着信息搜索日益成为互联网的主要应用,搜索引擎技术正成为计算机工业界和学术界争相研究和开发的热点。本文主要介绍搜索引擎的基本原理、工作过程及技术发展趋势。     

面向酒店评论的情感分析模型

提出一种基于文本特征的专门面向酒店评论领域的情感分析模型,通过构建酒店评论领域专用情感词典,并结合酒店评论的句式特征、语法特点,解决了通用情感分析模型应用在酒店评论领域时,情感匹配不全面,情感值计算不精确等问题.本文实验结果表明,基于文本特征的情感分析模型能对酒店评论情感分析取得较好的分类效果.     

领域同义特征词的统计规律及其在情感分析上的应用研究

为了研究同义特征词的规律及其对情感分析的影响,基于大规模语料库的统计学习方法,发现如果将每一个特征词的所有同义特征词组成特征词组,词组中的同义特征词按照自身在语料库中出现的频率从大到小排列,那么同义特征词在词组中出现的频率与该词在词组中的名次存在幂函数关系：y＝β0xβ1。将特征代表词的同义特征词所对应的情感词加入到该特征代表词的情感词集中,可以有效提高特征代表词的情感分析的准确性。     

挖掘中文网络客户评论的产品特征及情感倾向*

为探索中文客户评论中的产品特征及相关情感倾向的挖掘,以帮助生产商和服务商改进产品、改善服务,提高竞争力,提出采用基于Apriori算法的非监督型产品特征挖掘算法,结合监督型情感分析技术,实现对于评论中产品特征及其情感倾向的综合信息挖掘;并根据用户的关注权重将产品特征和情感倾向进行排列。采用几种从互联网下载的真实产品评论语料,对该方法进行了数据实验,实验结果初步验证了该方法的有效性。