A framework for fake review detection in online consumer electronics retailers

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.     

Temporal burstiness and collaborative camouflage aware fraud detection

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.     

Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures

Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes.     

在线评论对消费者购买行为的影响研究——消费者学习视角

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。     

Polarity shift detection,elimination and ensemble: A three-stage model for document-level sentiment analysis

The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of document-level sentiment classification. We first split each document into a set of subsentences and build a hybrid model that employs rules and statistical methods to detect explicit and implicit polarity shifts, respectively. Secondly, we propose a polarity shift elimination method, to remove polarity shift in negations. Finally, we train base classifiers on training subsets divided by different types of polarity shifts, and use a weighted combination of the component classifiers for sentiment classification. The results on a range of experiments illustrate that our approach significantly outperforms several alternative methods for polarity shift detection and elimination.     

E-word of mouth sentiment analysis for user behavior studies

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.     

面向B2C电商网站的消费者评论有用性评价模型研究

[目的/意义]目前各大电子商务网站产生了海量的评论信息，对于消费者而言，查阅和分析这些信息将面临巨大的挑战。因此，有必要对评论的有用性进行综合评价，为消费者过滤出真正有价值的内容。[方法/过程]为此，本文提出并研究了一种在线消费者评论的有用性评价模型，为消费者的网购决策提供支持。该模型主要基于分类算法，识别在线消费者评论的有用性，并按其概率值大小进行排序。根据在线消费者评论的特点，提取了一系列分类特征用于其有用性评价，然后利用支持向量机对评论进行分类并从中识别有用的记录。利用来自B2C电子商务网站的3个在线消费者评论数据集（手机、女鞋、糖果巧克力）对提出的模型进行实证分析。[结果/结论]研究结果显示，该模型能够量化地评价在线消费者评论的有用性并对其进行有效的分类排序。该模型主要依赖语义特征进行排序，而对非语义特征的依赖较少。通过选择合适的概率阈值，能够缩小验证空间，并显著提升分类精确度。     

不同平台在线评论消费者感知差异研究——产品类型的调节作用

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。     

Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

The development of Management Information Systems (MIS) is impossible without the use of machine learning (ML). It's a type of Artificial Intelligence (AI) that makes predictions using statistical models. When it comes to financial analysis, there are numerous risk-related concerns to contend with today (FI). In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. To better serve its customers, the financial sector can now save borrower data according to specific criteria thanks to MIS. In fact, there is a large amount of data about debtors, making load management a difficult task. ML can examine millions of data sets in a short period of time without being explicitly programmed to improve the results. This type of algorithm can aid financial institutions in making grant selections for their clients. For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. Bayesian Belief Network, Neural Network, Decision trees, Naïve Bayes, and Nearest Neighbor has been compared for the purpose of classifying FI risks using the performance measures asfalse positive rate, true positive rate, true negative rate, false negative rate, accuracy, F-Measure, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Med AE, Receiver Operating Characteristic (ROC) area,Precision Recall Characteristic (PRC) area, and measures of PC.     

在线评论对消费者网络购买意愿影响的研究

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.     

Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion

In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.     

Information resources estimation for accurate distribution-based concept drift detection

Machine learning applications must continually utilize label information from the data stream to detect concept drift and adapt to the dynamic behavior. Due to the computational expensiveness of label information, it is impractical to assume that the data stream is fully labeled. Therefore, much research focusing on semi-supervised concept drift detection has been proposed. Despite the large research effort in the literature, there is a lack of analysis on the information resources required with the achievable concept drift detection accuracy. Hence, this paper aims to answer the unexplored research question of “How many labeled samples are required to detect concept drift accurately?” by proposing an analytical framework to analyze and estimate the information resources required to detect concept drift accurately. Specifically, this paper disintegrates the distribution-based concept drift detection task into a learning task and a dissimilarity measurement task for independent analyses. The analyses results are then correlated to estimate the required number of labels within a set of data samples to detect concept drift accurately. The proximity of the information resources estimation is evaluated empirically, where the results suggest that the estimation is accurate with high amount of information resources provided. Additionally, estimation results of a state-of-the-art method and a benchmark data set are reported to show the applicability of the estimation by proposed analytical framework within benchmarked environments. In general, the estimation from the proposed analytical framework can serve as guidance in designing systems with limited information resources. This paper also hopes to assist in identifying research gaps and inspiring new research ideas regarding the analysis of the amount of information resources required for accurate concept drift detection.     

数字内容产业的自我规制研究

构建了包含政府、运营商、提供商和消费者等4个利益主体的自我规制体系.结果显示,运营商作为规制者,如果其曝光提供商不良行为的概率增加,提供商就需要向运营商输送更大的利益,从而合谋意愿降低;消费者投诉成本降低,虽然导致提供商向运营商输送的利益减少,但合谋行为被发现的概率增加,从而合谋意愿降低.串谋均衡时,运营商的边际监督激励低,高水平的欺诈不仅取决于低水平的监督,还取决于提供商的行贿成本.     

Candidate document retrieval for cross-lingual plagiarism detection using two-level proximity information

The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection.     

海杂波统计特性分析及海面动目标检测

利用IPIX雷达回波数据分析了海杂波的统计特性.并利用LFM信号在分数阶Fourier域良好的能量聚集性,提出基于分数阶Fourier变换的海面动目标检测方法.此方法能较好的聚集动目标回波能量,而对海杂波回波的能量聚集不明显,可以较好的检测出动目标.最后采用实测海杂波数据做了仿真分析,证实了此方法的有效性.     

Expert group formation using facility location analysis

In this paper, we propose an optimization framework to retrieve an optimal group of experts to perform a multi-aspect task. While a diverse set of skills are needed to perform a multi-aspect task, the group of assigned experts should be able to collectively cover all these required skills. We consider three types of multi-aspect expert group formation problems and propose a unified framework to solve these problems accurately and efficiently. The first problem is concerned with finding the top k experts for a given task, while the required skills of the task are implicitly described. In the second problem, the required skills of the tasks are explicitly described using some keywords but each expert has a limited capacity to perform these tasks and therefore should be assigned to a limited number of them. Finally, the third problem is the combination of the first and the second problems. Our proposed optimization framework is based on the Facility Location Analysis which is a well known branch of the Operation Research. In our experiments, we compare the accuracy and efficiency of the proposed framework with the state-of-the-art approaches for the group formation problems. The experiment results show the effectiveness of our proposed methods in comparison with state-of-the-art approaches.     

Detecting collusive spammers with heterogeneous graph attention network

Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network. First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance, we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon, Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively.     

认知无线电中基于一致平均的分布式合作算法

认知无线电中首要的任务是认知用户感知外界无线环境进行频谱检测和估计,目前合作式的频谱检测技术由于其良好的检测性能广受人们关注。为了提高认知用户对主用户的检测性能,提出了一种基于一致平均的分布式合作方法,优化了联合检测性能。与传统的AND算法、OR算法比较,仿真结果呈现了较好的检测性能,大大降低了误检概率和虚警概率,提高了检测概率,从而能充分利用空闲频谱。     

基于评论挖掘的用户购买行为因果事理图谱分析

【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。     

Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge amount of reviews, sentiment analysis plays a basic role to extract significant information and overall sentiment orientation of reviews. In this paper, we present a deep-learning-based method to classify a user's opinion expressed in reviews (called RNSA).To the best of our knowledge, a deep learning-based method in which a unified feature set which is representative of word embedding, sentiment knowledge, sentiment shifter rules, statistical and linguistic knowledge, has not been thoroughly studied for a sentiment analysis. The RNSA employs the Recurrent Neural Network (RNN) which is composed by Long Short-Term Memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about the word are vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the following drawbacks: words with similar semantic context but opposite sentiment polarity; contextual polarity; sentence types; word coverage limit of an individual lexicon; word sense variations. To verify the effectiveness of our work, we conduct sentence-level sentiment classification on large-scale review datasets. We obtained encouraging result. Experimental results show that (1) feature vectors in terms of (a) statistical, linguistic and sentiment knowledge, (b) sentiment shifter rules and (c) word-embedding can improve the classification accuracy of sentence-level sentiment analysis; (2) our method that learns from this unified feature set can obtain significant performance than one that learns from a feature subset; (3) our neural model yields superior performance improvements in comparison with other well-known approaches in the literature.