共查询到20条相似文献，搜索用时 31 毫秒
1.
【目的/意义】互联网数据中隐藏着的消费心理、消费需求等消费者情报对提升企业竞争力意义重大。对用
户购买行为产生及演进机制的发掘,不仅能让企业掌握更多自身产品和服务中的具体细节信息,还能从本质上发
现用户的需求偏好,推进企业实施科学经营决策。【方法/过程】本文提出一种利用因果事理图谱的消费者情报获取
方法,以京东平台手机在线评论数据源为例,首先通过利用基于规则和依存句法分析结合的自然语言处理技术对
数据源之间的因果关系变量进行识别和事件知识抽取,再结合LDA模型进行事件聚类,最后利用Gephi可视化等
方法实现对用户购买行为的起源与发展机制等特征的识别与呈现,探测用户潜在需求偏好。【结果/结论】结果显
示,用户购买手机的行为是一系列严密的因果事理逻辑演进过程,包括买前需求、购买决策、买后评价三个递进阶
段,用户经历产生购买需求;多维需求驱动购买决策演化;最后是否获得对应需求服务的过程影响满意度的评价。
【创新/局限】采用事理图谱的用户购买行为分析,为拓展大数据情报挖掘方法提供了借鉴。但基于规则的事件知
识抽取受数据库限制,导致该方法实施效率受到一定程度影响。 相似文献
2.
探索在线评论相关领域中的研究主题以及它们之间的结构关系.从Web of Science核心数据库提取2009-2013年共113篇文献,通过共词分析确定了41个关键词,然后运用社会网络分析法识别了在线评论内容分析、在线评论深度挖掘、在线评论服务响应、在线评论行为研究、在线评论系统与社交媒体、在线评论与消费者决策、在线评论质量研究7个研究主题,最后提出一个新的研究框架为当前的研究提供参考. 相似文献
3.
【目的/意义】随着网络购物的普及,在线评论成为影响消费者、销售者和生产者决策的重要数据。大数据
时代,在线评论呈现出多源异构、爆发式增长的特点,难以为用户的购买决策和商家竞争提供有力的情报支撑。【方
法/过程】本文利用多源异构的在线评论数据构建知识图谱,提出了一种基于多源异构数据构建知识图谱的框架,
模式层构建围绕在线评论的信源、内容以及形式构建,最终形成知识图谱的概念框架,并运用word2vec从多源异构
文本中获取实体、关系和属性,并进行数据融合与知识图谱分析。【结果/结论】实验部分以手机商品在线评论为例,
验证了本文所构建的知识图谱对在线评论相关研究及挖掘的有效性,研究结果揭示了多源异构在线评论数据的特
点,为大数据环境下在线评论信息组织、展示和挖掘提供了新的研究视角。【创新/局限】运用知识图谱对在线评论
进行描述,有效解决信息过载、多源异构信息融合等问题。本文采用半自动化的方式构建知识图谱,未来考虑引入
无监督的方法提高构建效率。 相似文献
4.
【目的/意义】负面在线评论已成为商家重要的经营决策信息,对了解客户消费满意度、改善产品和服务质量
具有重要意义。【方法/过程】该文将情感分析和关键词抽取相结合,提出一种基于BiGRU-CNN 和 TextRank的在
线评论负面关键词抽取方法,即首先对在线评论文本数据进行清洗,然后构建 BiGRU- CNN 情感分类模型对在
线评论进行情感分析,最后采取TextRank 方法抽取情感分析得到的负面评论中的关键词。利用这种方法,对十个
产品与服务类别的6万余条消费者在线评论文本数据进行实证分析。【结果/结论】实验结果表明,该方法能准确判
别客户负面在线评论情感倾向,F1值达92.41%,并且负面在线评论关键词抽取结果能较好帮助商家完善产品质量
和服务。【创新/局限】提出一种结合双向GRU 和CNN 结合的情感分类模型,在此基础上基于TextRank 方法抽取
情感分析得到的负面评论中的关键词,进一步提升模型对于在线评论情感分析的准确性。 相似文献
5.
[目的/意义]针对网络产品评论细粒度意见挖掘的研究进展进行分析和总结,在明确其主要任务的基础上,探讨涉及的关键技术、研究成果以及未来发展趋势,为该领域研究未来的发展提供建议。[方法/过程]本文主要采用文献综述的方法,对国内外相关研究进展进行分析和归纳,由粗粒度意见挖掘引申到细粒度意见挖掘,在明确细粒度意见挖掘主要任务的基础上,重点针对其关键技术和研究进展进行总结。[结果/结论]本文明确了网络产品评论细粒度意见挖掘的主要任务,包括主客观句分类、评价要素抽取和情感极性计算,总结了各个任务涉及的关键技术。 相似文献
6.
《Information processing & management》2019,56(4):1234-1244
The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test. 相似文献
7.
为了理解在线评论对消费者网络购买意愿影响的主要动因,基于计划行为理论、技术接受模型理论和网购顾客消费体验对在线评论行为作用模型,构建在线评论对消费者网络购买决策影响的动因模型,并提出若干假设,最后通过数据采集,采用AMOS21.0软件进行数据分析,对模型和假设进行了实证研究,统计分析结果表明: 消费者——网站关系、在线评论数量、在线评论质量、在线评论接收者专业能力、在线评论接收者涉入度、在线评论接收者感知风险影响消费者网络购买意愿,在线评论者资信度和在线评论的时效性影响不显著.基于此,本文对结果进行了讨论,并对消费者和网商营销提出了建议. 相似文献
8.
9.
Ding Xiao Yugang Ji Yitong Li Fuzhen Zhuang Chuan Shi 《Information processing & management》2018,54(6):861-873
Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines. 相似文献
10.
在线商品评论对产品销量影响研究 总被引：3，自引：0，他引：3
作为一种新型的口碑传播方式,在线产品评论成为了消费者和商家了解产品质量和服务的最为重要的信息来源。在线产品评论哪些因素影响到消费者的购买决策,对产品的销量产生多大的影响已经成为人们关注的重要问题。通过对在线手机评论研究发现,"在线评论数量"、"商品的关注度"对在线手机销量有显著性影响,更为重要的是我们发现"评论的时效性"和"顾客认为评论的有用率"对手机的销量也有非常重要的显著性影响,而"评论的正负情感倾向性"等对产品的销量无明显影响。 相似文献
11.
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
12.
【目的/意义】为了协助商家和平台获取移动商务在线评论中的用户需求,解决在线评论过载导致用户需求
提取困难等问题。【方法/过程】本文首先获取原始在线评论数据集进行文本预处理和清洗;然后,深入语义层面基
于改进后的 Canopy-Kmeans算法实现用户需求聚合;最后,以聚合结果为层级指标设计 Kano问卷,用重要性判别
方法和用户满意度指数优化用户需求分类标准,实现用户需求的高效聚合和精准挖掘。【结果/结论】通过实验结果
对比分析发现与基于语义的传统聚类方法相比,本文设计的移动商务用户需求聚合与挖掘方法的聚类结果更清晰
合理,能够获取更精准和细化的用户需求。【创新/局限】借助Word2vec模型从语义的视角分析用户需求,提出基于
Canopy-Kmeans算法的用户需求聚合挖掘模型,但选取的研究对象和数据规模较为有限,下一步将扩大在线商品
评论的研究范围及实验数据规模。 相似文献
13.
[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
14.
《Information processing & management》2022,59(1):102784
Nowadays, online word-of-mouth has an increasing impact on people's views and decisions, which has attracted many people's attention.The classification and sentiment analyse in online consumer reviews have attracted significant research concerns. In this thesis, we propose and implement a new method to study the extraction and classification of online dating services(ODS)’s comments. Different from traditional emotional analysis which mainly focuses on product attribution, we attempted to infer and extract the emotion concept of each emotional reviews by introducing social cognitive theory. In this study, we selected 4,300 comments with extremely negative/positive emotions published on dating websites as a sample, and used three machine learning algorithms to analyze emotions. When testing and comparing the efficiency of user's behavior research, we use various sentiment analysis, machine learning techniques and dictionary-based sentiment analysis. We found that the combination of machine learning and lexicon-based method can achieve higher accuracy than any type of sentiment analysis. This research will provide a new perspective for the task of user behavior. 相似文献
15.
本文通过实验法探索消费者对于不同平台评论的感知差异以及产品类型的调节作用。首先,在已有研究的基础上对不同平台以及不同产品类型的特征进行归纳,并提出研究假设;然后,通过3*2析因设计,即3种不同平台(卖家网站、第三方平台和消费者建立平台)*2种产品类型(搜索品和体验品)共6个实验组,并利用问卷方式在线搜集数据来进行假设检验,研究发现,消费者对第三方平台和消费者自建平台的评论的感知可信度高于商家平台,并且对于体验品,商家平台与第三方平台以及商家平台与消费者自建平台之间的消费者感知可信度存在显著差异;最后,结合研究发现展开了分析和讨论。 相似文献
16.
[目的/意义] 提出一种基于在线产品评论的竞争情报挖掘框架,为企业改进产品设计和制定竞争策略提供参考。[方法/过程] 利用Word2vec技术构建产品特征词集合,识别用户评论主题特征。然后使用情感分析方法对评论文本进行分类,得到特征维度的评论情感。最后从产品主题特征和情感态度特征两方面进行数据分析,并以可视化结果呈现。[结果/结论] 以汽车行业的评论数据为例进行实验,结果表明该方法能够有效提取产品情报信息,帮助企业有效识别自身品牌及竞争对手的优势和劣势,为大数据环境下的竞争情报挖掘提供方法指导。 相似文献
17.
Consumers evaluate products through online reviews, in addition to sharing their product experiences. Online reviews affect product marketing, and companies use online reviews to investigate consumer attitudes and perceptions of their products. However, when analyzing a review, it is often the case that specific contexts are not taken into consideration and meaningful information is not obtained from the analysis results. This study suggests a methodology for analyzing reviews in the context of comparing two competing products. In addition, by analyzing the discriminative attributes of competing products, we were able to derive more specific information than an overall product analysis. Analyzing the discriminative attributes in the context of comparing competing products provides clarity on analyzing the strengths and weaknesses of competitive products and provides realistic information that can help the company's management activities. Considering this purpose, this study collected a review of the BB Cream product line in the cosmetics field. The analysis was sequentially carried out in three stages. First, we extracted words that represent discriminative attributes by analyzing the percentage difference of words. Second, different attribute words were classified according to the meaning used in the review by using latent semantic analysis. Finally, the polarity of discriminative attribute words was analyzed using Labeled-LDA. This analysis method can be used as a market research method as it can extract more information than a traditional survey or interview method, and can save cost and time through the automation of the program. 相似文献
18.
用户在线消费评论是电子商务平台客户评论系统的核心内容之一,也是潜在消费者网络购买决策的重要依据。本文运用内容分析和对应分析法对B2C电子商务平台用户评级和用户评论内容进行比较研究。结果表明,不同网站用户评论存在显著差异,用户评论受商品类别的影响和中美网站用户评论存在文化差异。研究结论对完善平台用户评论管理和营销管理具有重要的理论和实践价值。 相似文献
19.
Mohammad Tubishat Norisma Idris Mohammad A.M. Abushariah 《Information processing & management》2018,54(4):545-563
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. 相似文献
20.
为了理解在线评论对消费者购买行为的影响,文章采集淘宝网400多家店铺的在线评论信息,基于S-O-R模型(Stimulus-Organism-Response Model),从消费者学习的角度,研究体验型商品的在线评论信息对消费者购买行为的影响。采用SPSS 19.0软件进行数据分析,对假设进行实证研究,统计结果表明,好评数量、描述评分、有图片评论数量、追加评论数量和累计评论数量对消费者购买行为造成影响,中评数量、差评数量、物流评分和服务评分影响效果不显著。文章最后提出了建议与不足。 相似文献