【摘要】：web2.0技術(shù)的迅速崛起,使越來越多的用戶喜歡在電商平臺和點(diǎn)評網(wǎng)站上發(fā)表評論,分享他們對于產(chǎn)品和服務(wù)的觀點(diǎn)和感受,這些用戶發(fā)布的評論信息無論是對消費(fèi)者還是商家都是至關(guān)重要的,因?yàn)檫@些評論包含著大量用戶對產(chǎn)品或者服務(wù)質(zhì)量的描述。但是受利益的驅(qū)使,一些不法商家通過雇傭虛假評論者發(fā)布不真實(shí)的評論來提高自己的信譽(yù)或者詆毀競爭對手的信譽(yù),以達(dá)到誤導(dǎo)消費(fèi)者購物決策的目的。這種行為不僅誤導(dǎo)消費(fèi)者的購物決策,而且還嚴(yán)重影響了電子商務(wù)的健康發(fā)展,所以盡早發(fā)現(xiàn)虛假評論并在最大程度上減少它們的影響是刻不容緩的。近年來,虛假評論檢測已經(jīng)成為一個(gè)熱門的研究領(lǐng)域。研究者常常通過分析文本極性和評分模式來發(fā)現(xiàn)虛假攻擊,這些通用的檢測方法能夠輕松地檢測出常規(guī)的虛假攻擊,但是卻很難有效識別出那些把自己偽裝成真實(shí)用戶的虛假評論者。傳統(tǒng)的單一維度檢測算法未能考慮多個(gè)評論特征之間的潛在影響,致使準(zhǔn)確率不高,為此本文提出了一種基于動態(tài)多模網(wǎng)絡(luò)的虛假評論檢測算法,并進(jìn)行了較為深入的研究工作。本文主要工作及創(chuàng)新點(diǎn)如下:(1)提出了一種融合動態(tài)多模網(wǎng)絡(luò)的虛假評論探測方法。該方法首先構(gòu)建了包含評論、評論者、商品和商家的四維網(wǎng)絡(luò);然后提出了評論忠實(shí)度、評論者信譽(yù)度、商品優(yōu)質(zhì)度和商家可信度概念并對其量化;緊接著使用譜聚類算法探討了四類節(jié)點(diǎn)之間的聯(lián)系,最后設(shè)計(jì)了一個(gè)迭代計(jì)算模型,通過迭代計(jì)算揭示了四維網(wǎng)絡(luò)之間的動態(tài)交互影響。使用該方法可以同時(shí)準(zhǔn)確地檢測出虛假評論、虛假評論者和不良商家。(2)提出了一種基于情感強(qiáng)度的虛假評論檢測算法,該方法主要通過自然語言處理技術(shù)分析評論文本情感極性。在本文中,我們的方法主要有以下幾點(diǎn)創(chuàng)新:首先,我們使用領(lǐng)域詞典挖掘出評論類別,并考慮了關(guān)聯(lián)詞對文本極性的影響;其次,本文簡化了實(shí)驗(yàn)數(shù)據(jù)的采集與處理工作,通過分析數(shù)據(jù)發(fā)現(xiàn)了5個(gè)重要的虛假評論檢測特征;最后,使用邏輯回歸模型將5個(gè)量化后的特征融合在一起,并訓(xùn)練出一個(gè)有效的虛假評論分類模型。該方法是計(jì)算多模網(wǎng)絡(luò)中評論忠實(shí)度的重要前提。(3)提出了一種改進(jìn)的基于用戶信譽(yù)的虛假評論檢測算法。首先,使用矩陣補(bǔ)全理論把低秩稀疏的用戶-項(xiàng)目評分矩陣填充,其次,構(gòu)建用戶信譽(yù)評估模型;最后,本文選擇了更加合理的預(yù)估標(biāo)準(zhǔn),并且細(xì)化了群組規(guī)模相同而評分不同的用戶信譽(yù),使用top-k算法判定信譽(yù)值最低的k個(gè)用戶為虛假評論者。該方法對于計(jì)算多模網(wǎng)絡(luò)的用戶信譽(yù)是至關(guān)重要的。
[Abstract]:The rapid rise of web2.0 technology has made more and more users like to comment on e-commerce platforms and comment sites to share their views and feelings about products and services. Comments posted by these users are critical to both consumers and businesses because they contain a large number of user descriptions of product or service quality. However, driven by interests, some illegal businesses use false reviewers to release false comments to improve their credibility or discredit competitors, in order to mislead consumers to make shopping decisions. This behavior not only misleads consumers' shopping decisions, but also seriously affects the healthy development of electronic commerce, so it is urgent to find false comments as soon as possible and minimize their impact. In recent years, false comment detection has become a hot research field. Researchers often detect false attacks by analyzing text polarity and scoring patterns, which can easily detect conventional false attacks. But it's hard to identify false commentators who pretend to be real users. The traditional single dimensional detection algorithm fails to take into account the potential influence of multiple comment features, which leads to low accuracy. Therefore, this paper proposes a false comment detection algorithm based on dynamic multi-mode network. And has carried on the more thorough research work. The main work and innovations of this paper are as follows: (1) A new method of false comment detection based on dynamic multimode network is proposed. In this method, a four-dimensional network including comments, reviewers, commodities and merchants is constructed, and then the concepts of comment fidelity, commenters reputation, commodity quality and merchant credibility are proposed and quantified. Then the relationship between the four kinds of nodes is discussed by using spectral clustering algorithm. At last, an iterative computing model is designed to reveal the dynamic interaction between the four dimensional networks. Using this method, false comments, false reviewers and bad merchants can be detected accurately at the same time. (2) A false comment detection algorithm based on emotional intensity is proposed. This method mainly uses natural language processing technology to analyze the emotional polarity of comment text. In this paper, our method mainly has the following innovations: first, we use the domain dictionary to mine out the comment categories, and consider the influence of the relevance words on the polarity of the text; Secondly, this paper simplifies the collection and processing of experimental data, and finds five important features of false comment detection by analyzing the data. Finally, the five quantized features are fused by using the logical regression model, and an effective false comment classification model is trained. This method is an important prerequisite for computing the fidelity of comments in multimode networks. (3) an improved algorithm for detecting false comments based on user reputation is proposed. Firstly, the matrix complement theory is used to fill the low rank sparse user-item scoring matrix. Secondly, the user reputation evaluation model is constructed. Finally, we select more reasonable prediction criteria, and refine the reputation of users with the same group size and different ratings. We use top-k algorithm to determine k users with the lowest reputation as false reviewers. This method is very important for computing the user reputation of multimode network.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 何鳳英;;基于語義理解的中文博文傾向性分析[J];計(jì)算機(jī)應(yīng)用;2011年08期

2 唐波;陳光;王星雅;王非;陳小慧;;微博新詞發(fā)現(xiàn)及情感傾向判斷分析[J];山東大學(xué)學(xué)報(bào)(理學(xué)版);2015年01期

3 史加榮;鄭秀云;周水生;;矩陣補(bǔ)全算法研究進(jìn)展[J];計(jì)算機(jī)科學(xué);2014年04期

4 賈洪杰;丁世飛;史忠植;;求解大規(guī)模譜聚類的近似加權(quán)核k-means算法[J];軟件學(xué)報(bào);2015年11期

，

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