本文選題：垃圾評論　切入點：誠實評論　出處：《南京郵電大學(xué)》2014年碩士論文

【摘要】：購物網(wǎng)站評論為消費(fèi)者比較商品的質(zhì)量、店家的服務(wù)等提供了有價值的信息。然而垃圾評論者通過發(fā)表虛假的、不公正的評論來誤導(dǎo)消費(fèi)者�，F(xiàn)存許多基于評論者行為特征的垃圾評論檢測方法，但這些方法對于有意模仿正常評論者行為的垃圾評論者是無法檢測的。 垃圾評論檢測工作之所以困難很大，是因為垃圾評論者可以輕松的發(fā)表與正常評論相似的評論，所以單單從評論或評論者出發(fā)的檢測方法性能很低。本文首先通過層次分析法得到店家的可信度、評論的文本等特征得到評論的誠實度，然后根據(jù)評論關(guān)系圖分析評論者、評論、店家的交互關(guān)系，最終使用Logistic進(jìn)行分類。 評論關(guān)系圖由三種類型的節(jié)點構(gòu)成，評論者、評論、店家，分析得出三者的交互關(guān)系：評論者所發(fā)表的誠實的評論越多，他的可靠度就越高；店家所得到的來自可靠評論者的誠實正面評論越多，它的可信度也越高；評論和其它周圍誠實評論的相似度越高，該評論的誠實度也就越高。這是在垃圾評論檢測領(lǐng)域首次提出基于交互關(guān)系的檢測方法，解決了檢測信息量較少的局限。實驗證明，本文提出的檢測方法檢測出的垃圾評論類型更復(fù)雜、更精細(xì)，同時在精確率、召回率等各項指標(biāo)均有所改善，而且大大縮短了計算程序的運(yùn)行時間。
[Abstract]:Shopping site reviews provide valuable information for consumers to compare the quality of goods, store services, and so on. Many existing spam review detection methods based on the behavior characteristics of commenters are not detectable for spam reviewers who are interested in imitating normal reviewers' behavior. The reason why spam reviews are difficult to detect is that spam reviewers can easily post comments similar to normal ones. Therefore, the performance of the detection method is very low. Firstly, the credibility of the shop owner is obtained by AHP, the honesty of the comment is obtained by the text of the comment, and then the reviewer is analyzed according to the comment relationship graph. Reviews, store interactions, and ultimately use Logistic for classification. The review diagram is composed of three types of nodes, the reviewer, the commentator, the shopkeeper, and the analysis shows that the more honest comments the reviewer makes, the higher his reliability; The more honest positive comments a store gets from reliable commentators, the higher its credibility; the higher the similarity between reviews and other honest comments around them, This is the first time an interactive detection method has been put forward in the field of spam review detection, which solves the limitation of less information. The detection method presented in this paper is more complex and more precise in detecting garbage comments. At the same time, the accuracy rate and recall rate are improved, and the running time of the calculation program is shortened greatly.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.092

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相關(guān)碩士學(xué)位論文 前1條

1 王云;基于關(guān)系的垃圾評論檢測方法[D];南京郵電大學(xué);2014年

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本文編號：1679041