發(fā)布時(shí)間：2018-04-09 12:46

  本文選題：搜索型商品　切入點(diǎn)：在線評(píng)論有用性　出處：《江蘇大學(xué)》2017年碩士論文

【摘要】：隨著電子商務(wù)的迅猛發(fā)展,消費(fèi)者使用網(wǎng)絡(luò)購(gòu)物已漸漸成為常態(tài)化,由于網(wǎng)絡(luò)購(gòu)物的信息不對(duì)稱,商品在線評(píng)論成為人們獲取商品和服務(wù)信息的重要來源,同時(shí)也是消費(fèi)者分享商品使用體驗(yàn)的平臺(tái)。由于在線評(píng)論存在大量性、多樣性和復(fù)雜性等特點(diǎn),客觀上要求及時(shí)、正確挖掘出有效評(píng)論信息。因此,探究在線評(píng)論有用性的影響因素,并在此基礎(chǔ)上建立評(píng)論有用性識(shí)別模型,為今后建立更加靈活有效的評(píng)論評(píng)價(jià)系統(tǒng)具有重要的指導(dǎo)意義。本文以抓取到的亞馬遜購(gòu)物網(wǎng)站6種搜索型商品共計(jì)1042條在線評(píng)論為研究對(duì)象,以信息采納模型、有用信息的特征以及消費(fèi)者購(gòu)買行為為理論基礎(chǔ),在已有文獻(xiàn)的基礎(chǔ)上,從評(píng)論內(nèi)容屬性和評(píng)論者屬性兩個(gè)方面構(gòu)建評(píng)論有用性影響因素模型。然后以評(píng)論長(zhǎng)度、評(píng)論內(nèi)容完整性、評(píng)論及時(shí)性、評(píng)論情感強(qiáng)度和評(píng)論來源的可信度為自變量,其中評(píng)論內(nèi)容完整性又細(xì)分為服務(wù)信息、物流信息和商品信息,以評(píng)論有用性為因變量,通過建立Tobit回歸模型來探究評(píng)論有用性的影響因素,并對(duì)驗(yàn)證通過的因素進(jìn)行相關(guān)性分析。最后根據(jù)評(píng)論有用性的影響因素建立評(píng)價(jià)指標(biāo)體系,選取樸素貝葉斯算法、支持向量機(jī)算法和C4.5決策樹算法分別建立分類模型,將評(píng)論分為“有用”和“無用”兩類評(píng)論,并根據(jù)分類模型分類的準(zhǔn)確率和效率,選取最優(yōu)分類模型。研究結(jié)果表明,評(píng)論字?jǐn)?shù)越多、評(píng)論中提及商家或平臺(tái)的服務(wù)信息、評(píng)論中提及的商品屬性越多、主觀性表達(dá)越豐富、評(píng)論星級(jí)評(píng)分越低、評(píng)論者排名越靠前,則消費(fèi)者評(píng)論的感知有用性越高,而評(píng)論中是否提及物流信息和評(píng)論發(fā)表的天數(shù)對(duì)評(píng)論有用性的影響不顯著;評(píng)論長(zhǎng)度與評(píng)論內(nèi)容的完整性、評(píng)論主觀性呈正相關(guān)關(guān)系,與評(píng)論情感強(qiáng)度無關(guān),評(píng)論者排名與評(píng)論長(zhǎng)度、評(píng)論中的商品信息呈負(fù)相關(guān)關(guān)系,與評(píng)論情感強(qiáng)度無關(guān);支持向量機(jī)算法的分類精確度最高,綜合準(zhǔn)確率達(dá)到74.28%,F1測(cè)度值為0.736,分類過程耗時(shí)0.28秒,因此,選擇支持向量機(jī)算法作為搜索型商品在線評(píng)論有用性的分類模型。
[Abstract]:With the rapid development of electronic commerce, consumers' online shopping has gradually become a norm. Because of the information asymmetry of online shopping, online commodity review has become an important source for people to obtain information about goods and services.At the same time, it is also a platform for consumers to share the experience of using goods.Due to the large quantity, diversity and complexity of online comments, it is necessary to mine the effective comment information correctly and timely.Therefore, it is of great significance to explore the factors influencing the usefulness of online reviews and to establish a model for the identification of comment usefulness, which will be helpful to the establishment of a more flexible and effective comment evaluation system in the future.In this paper, a total of 1042 online reviews of 6 kinds of search items on Amazon shopping website are taken as the research object. Based on the information adoption model, the characteristics of useful information and the purchasing behavior of consumers, and on the basis of the existing literature, this paper makes a research on the information adoption model of Amazon shopping website.The influence factors model of comment usefulness is constructed from two aspects: comment content attribute and reviewer attribute.Then the length of the comment, the integrity of the content of the comment, the timeliness of the comment, the emotional intensity of the comment and the credibility of the source of the comment are taken as independent variables, in which the integrity of the comment content is subdivided into service information, logistics information and commodity information.Based on the dependent variable of the usefulness of comments, the Tobit regression model is established to explore the influencing factors of the usefulness of comments, and the correlation analysis of the factors verified by the model is carried out.Finally, the evaluation index system is established according to the influential factors of comment usefulness, and the naive Bayesian algorithm, support vector machine algorithm and C4.5 decision tree algorithm are selected to establish classification models respectively, and the comments are divided into two categories: "useful" and "useless".According to the accuracy and efficiency of classification model, the optimal classification model is selected.The results showed that the more the number of words in the comments, the more the service information of the merchant or platform was mentioned in the comments, the more attributes of goods were mentioned in the comments, the more subjective expression was, the lower the rating of the comments, the higher the ranking of the reviewers.The perceived usefulness of the consumer comment is higher, but whether or not the logistics information and the number of days published in the comment have no significant influence on the comment usefulness, the length of the comment is positively correlated with the integrity of the content of the comment, and the subjectivity of the comment is positively correlated.It has nothing to do with the emotional intensity of the comment, the rank of the reviewer is negatively related to the length of the comment, the information of the product in the comment is negatively correlated with the emotional intensity of the comment, and the support vector machine algorithm has the highest classification accuracy.The synthetic accuracy rate of 74.28 / F _ 1 is 0.736, and the classification process takes 0.28 seconds. Therefore, support vector machine (SVM) algorithm is selected as the classification model for the usefulness of online reviews of search products.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F713.36;F713.55

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本文編號(hào)：1726512