产品评论挖掘研究综述

产品评论挖掘是以Web上用户发表的产品评论为挖掘对象,采用自然语言处理技术,从大量的文本数据中发现关于产品的功能和性能的评价信息的过程。产品评论挖掘是一个新兴的研究领域,是对自然语言描述的无结构数据进行数据挖掘的典型代表。产品评论中挖掘得到的信息不仅可以帮助生产厂商改进产品,还可以帮助用户合理的购买产品。对产品评论挖掘进行了全面深入地讨论,介绍了产品评论挖掘系统的通用框架,然后对产品特征提取、主观句定位、用户态度提取、态度极性判定、挖掘结果显示这5个子任务进行了详细地阐述,最后介绍了产品评论挖掘的最新方向。     

基于语义极性分析的餐馆评论挖掘

提出一种基于语义极性分析的餐馆评论挖掘方法。将餐馆的食物口味、环境、服务、价格作为其特征,以句子为单位对用户评论进行特征标注。将具有多个特征的复杂特征句划分为简单特征句,分析评论句的语义极性和极性强度。使用户可方便地了解其他用户对某个餐馆某种特征的评价,为用户消费提供了有力指导。     

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。     

基于Web评论的用户个人信息提取方法研究

企业收集和获取用户个人信息是其对用户行为进行分析以制定合理营销决策的前提。注意到当前,由于互联网的高度发展和普及,消费用户往往在Web上以评论文本的形式分享其消费习惯、消费偏好和消费体验,这些海量的评论文本中蕴含着极具价值的信息,为用户个人信息的收集提供良好的资源。针对传统企业收集用户个人信息的方法主要以人工为主导,自动化水平较低的问题,提出一种基于Web挖掘技术以网上评论文本为挖掘对象,对用户个人信息进行自动提取以自动分析用户行为的改进方法。企业可以通过此改进的用户个人信息提取方法对用户行为进行分析以自动获取消费用户对产品的反馈意见并制定有针对性的营销策略。     

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。     

中文产品评论的维度挖掘及情感分析技术研究

产品评论通常会描述产品的多个属性维度,单条评论中所描述的多个维度可能会有不同的维度情感。现实中,用户对产品不同的属性维度的关注度也不相同。反映到评论情感分析中,用户关注度越大的产品维度对评论的整体情感的影响也会越大。细粒度的评论维度挖掘和维度情感分析可以提供很多有价值的市场反馈信息和用户偏好信息。针对电商平台的中文产品评论文本,首先使用规则法抽取产品评论中所描述的维度信息,然后分别针对各个维度计算维度情感。进一步,提出了维度权重计算方法。最后,综合维度情感和维度权重计算评论的整体情感。使用来自于京东商城的真实评论数据集对所提方法进行了综合验证。实验结果表明,所提方法在维度挖掘、维度情感分析、维度权重计算以及整体情感分析方面具有很好的性能。     

基于观点挖掘的产品可用性建模与评价

提出基于观点挖掘的产品可用性建模与评价方法。以Web上的产品评论为数据,利用观点挖掘的方法从非结构化评论中抽取结构化数据,选取与可用性相关的产品特征,使用因子分析法提取影响产品可用性的公共因子,建立产品可用性模型。对产品可用性进行评价,结果表明,该方法可以有效地从用户角度发掘产品可用性中存在的缺陷,为产品设计提供依据。     

基于商品评论信息的特征挖掘

互联网上电商中存在着海量的评论信息,这些信息蕴含了重要的价值信息,一方面反映了用户对产品的评价,另一方面用户可以通过浏览评论信息决定是否购买。针对从海量的信息中挖掘重要信息,本文提出了通过LDA模型对评论信息中特征进行挖掘的方法。实验表明该方法能够有效的挖掘特征。     

基于特征聚类的网络评论信息挖掘研究

网络评论中的信息特征及情感倾向是一种重要信息,文章针对有的中文产品评论信息挖掘存在的不足,提出了一种基于词汇共现性的产品特征聚类技术与细粒度情感分析技术。在产品评论中,同类的产品属性会有多种多样的表述方式,文章将产品评论中的产品特征进行归类,且与以往基于句子的整体情感分析不同,提出了针对产品特征的更细粒度情感分析技术,并且对没有相应情感倾向的属性词做出合理处理。     

基于在线品牌社区意见领袖的用户关键需求挖掘

随着社会的快速发展以及技术的不断进步,人们生活节奏不断加快,对产品的需求也在快速发生着变化。在线评论是目前用户需求表达的重要渠道。为克服不加区分挖掘所有评论的传统用户需求挖掘方法耗时过长,难以聚焦用户关键需求的问题,从用户关键性与需求关键性的双关键性角度出发,研发了一种基于在线品牌社区意见领袖的用户关键需求挖掘方法以快速获取用户重要需求,简称KEY-DEMANDS-OL。该方法依据帕累托法则,依托意见领袖评论大数据,采用优化的情感程度及初始改进率,结合KANO模型对用户关键需求进行挖掘。该方法不仅考虑了程度副词的语义信息,提高了情感分析的准确率,而且能够完成意见领袖的生成内容与KANO模型的自动整合,实现用户关键需求的获取与分类。研究结果表明,与挖掘所有评论的传统方法相比,KEY-DEMANDS-OL可以快速获取用户的关键需求,为企业制定产品优化方案提供辅助决策支持。     

Textual factors in online product reviews: a foundation for a more influential approach to opinion mining

While data mining is well established in practice, opinion mining is still in its infancy, with issues in particular around the development of methodologies which effectively extract accurate, reliable, influential and useful information from the raw opinion data collected from informal product reviews. Current approaches adopt a single-variable approach, focusing on individual metrics—word length, the presence of keywords, or the overall semantic orientation of terms within the data—while neglecting to evaluate whether these individual artifacts are indicative of the tone of a given review. This approach has significant limitations when we move from trying to merely evaluate whether an online opinion is positive or negative, to trying to evaluate how likely it is that the opinion will influence others. Given this issue, one promising avenue would be to evaluate the general analysis approaches utilized by opinion mining algorithms and identified in the literature in terms of how accurately they reflect how people actually interpret and are influenced by electronic online reviews. Through interviewing and a follow up survey of 136?participants, the validity of the approach in terms of ascertaining the tone of a piece of text can be evaluated, as well as the identification of measurable factors within text which make a given opinionated text more or less influential in an online context, further facilitating the development of more effective multivariate opinion mining approaches. Furthermore, the identification of factors which make an online opinion text more or less persuasive helps to facilitate the development of opinion mining approaches which can evaluate how likely a review is to affect an individual’s decision making.     

Dynamical mining of ever-changing user requirements: A product design and improvement perspective

Previous studies carried out customer surveys by questionnaires to collect data for analyzing consumer requirements. In recent years, a large and growing body of literature has investigated the extraction of customer requirements and preferences from online reviews. However, since customer requirements change dynamically over time, traditional studies failed to obtain the change data of customer requirements and opinions based on sentiments expressed in reviews. In this paper, a new method for dynamically mining user requirements is proposed, which is used to analyze the changing behavior of product attributes and improve product design. Dynamic mining differs from the traditional need acquisition mainly in three aspects: (1) it involves dynamically mining user requirements over time (2) it adds changes in manufacturers’ opinions to the analysis (3) it allows for product improvement strategies based on the changing behavior of product attributes. First, text mining is adopted to collect customer and manufacturer review data for different time periods and extract product attributes. A Natural Language Processing tool is used to measure the importance weight and sentiment score of product attributes. Second, an approach for dynamically mining user requirements is introduced to classify product attributes and analyze the changes of attribute data in three categories over time. Finally, an improvement strategy for next-generation product design is developed based on the changing behavior of attributes. Moreover, a case study on vehicles based on online reviews was conducted to illustrate the proposed methodology. Our research suggests that the proposed approach can accurately mine customer requirements and lead to successful product improvement strategies for next-generation products.     

面向产品设计改进的在线评论挖掘

以竞争市场环境中的产品在线评论数据为研究对象,基于支持产品设计改进的视角,采用数据挖掘的方法与工具,开展面向产品设计改进的在线评论大数据分析研究。重点开展在线评论数据挖掘过程模型中的有用性建模和特征评价值情感分析。以某智能手机产品的在线评论数据为对象进行了实验,得到该产品各个属性的评价值,与更新换代后的产品属性进行比较,验证了此方法的有效性。     

Identifying influential reviewers for word-of-mouth marketing

The key to word-of-mouth marketing is to discover the potential influential nodes for efficiently spreading product impressions. In this paper, a framework combined with mining techniques, a modified PMI measure, and an adaptive RFM model is proposed to evaluate the influential power of online reviewers. An artificial neural network is adopted to identify the target reviewers and a well-developed trust mechanism is utilized for effectiveness evaluation. This proposed framework is verified by the data collected from Epinions.com, one of the most popular online product review websites. The experimental results show that the proposed model could accurately identify which reviewers to select to become the influential nodes. This proposed approach can be exploited in effectively carrying out online word-of-mouth marketing, which can save a lot of resources in finding customers.     

基于情感词抽取与LDA特征表示的情感分析方法

情感分析作为文本挖掘的一个新型领域,可用于分类、归纳用户发布的产品评论,从而有助于商家改善服务,提高产品质量;同时为其他消费者提供购买决策。本文提出一种基于情感词抽取与LDA特征表示的情感分析方法,对产品评论进行褒贬二元分类。在情感词抽取中,采用人工构造的情感词典对预处理之后的文本抽取情感词;用LDA模型建立文档的主题分布,以评论-主题分布作为特征,用SVM分类器进行分类。实验结果表明,本文方法在评论褒贬分类方面有着良好的效果。     

Product recommendation with latent review topics

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.     

网络意见挖掘、摘要与检索研究综述

网络上带有人的主观感情色彩的评论性文本反映了人们的意见、态度和立场,因而具有很大的利用价值.信息挖掘技术针对这些主观文本进行处理,获得有用的意见、结论和知识.首先介绍了意见挖掘出现的背景和应用意义,然后从词汇情感极性识别、粗粒度的情感分类、细粒度的意见挖掘与摘要、意见检索和相关语言资源与系统5个方面综述了研究历程和现状,最后总结了研究难点与研究趋势.     

基于贝叶斯网络的在线商品评价质量评估

随着电子商务领域的迅速发展,在线商品评价规模日益庞大,评价质量参差不齐,用户难以筛选有用评价信息做出购买决策,因此如何有效识别高质量评价信息成为重要议题。以在线商品评价的有用性投票为基础定义评价质量,使用贝叶斯网络表示在线商品评价的相似性及不确定性,通过对在线商品评价信息进行多维度特征统计,构建在线商品评价质量评估模型,使用概率推理机制对在线商品评价质量进行分类预测,并给出评价质量分类置信度。在真实数据集上验证模型有效性及高效性。     

A decision-making algorithm for online shopping using deep-learning–based opinion pairs mining and q-rung orthopair fuzzy interaction Heronian mean operators

In the process of online shopping, consumers usually compare the review information of the same product in different e-commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep-learning–based opinion mining and q-rung orthopair fuzzy interaction weighted Heronian mean (q-ROFIWHM) operators. First, in the proposed method, the deep-learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute-opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross-decision matrices, which are represented by the q-rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q-ROFIWHM operators are proposed to aggregate this cross-decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.