本文選題：在線評(píng)論　切入點(diǎn)：文本分析　出處：《內(nèi)蒙古大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著網(wǎng)絡(luò)購(gòu)物市場(chǎng)的快速發(fā)展以及相關(guān)購(gòu)物平臺(tái)與應(yīng)用的多樣性與便捷性,網(wǎng)上購(gòu)物給人們的生活帶來極大的便利,越來越多的人開始接受與選擇這種生活方式。但由于網(wǎng)絡(luò)商品的虛擬性和不可觸摸性,人們無(wú)法提前感知欲購(gòu)產(chǎn)品的質(zhì)量,于是很多人都傾向于依賴商品的在線評(píng)論而做出購(gòu)買決定。該情形又滋生了一些無(wú)良商家通過"好評(píng)返現(xiàn)"等各種手段制造出大量商品評(píng)論,這不但增加了消費(fèi)者篩選評(píng)論的時(shí)間成本,也可能會(huì)造成不必要的經(jīng)濟(jì)損失。因此,如何快速地識(shí)別高質(zhì)量的在線評(píng)論成為當(dāng)前在線評(píng)論內(nèi)容研究的新課題。本研究從在線評(píng)論內(nèi)容出發(fā),首先提取影響在線評(píng)論質(zhì)量的特征指標(biāo),然后構(gòu)建在線評(píng)論質(zhì)量評(píng)價(jià)指標(biāo)體系與模型,最后驗(yàn)證模型性能。具體內(nèi)容包括如下五個(gè)部分:(1)評(píng)論文本的有效性標(biāo)注。通過改進(jìn)基于長(zhǎng)度的自動(dòng)標(biāo)注算法和K-means算法,提出Lk-means算法對(duì)評(píng)論文本進(jìn)行有效性標(biāo)注,提取有效性這一指標(biāo);(2)指標(biāo)提取。將在線評(píng)論數(shù)據(jù)分為數(shù)值型和文本型兩類,二者結(jié)合可獲得完整性指標(biāo);并從數(shù)值型評(píng)論中提取評(píng)分?jǐn)?shù)據(jù),從文本型評(píng)論中提取信息量、可讀性、主題相關(guān)度和一致性這四個(gè)指標(biāo)。(3)構(gòu)建在線評(píng)論質(zhì)量評(píng)價(jià)指標(biāo)體系。根據(jù)改進(jìn)信息質(zhì)量評(píng)價(jià)的WRC指標(biāo)和研究中發(fā)現(xiàn)的數(shù)據(jù)質(zhì)量評(píng)價(jià)的1R3C指標(biāo),提出本研究的1W2R3C評(píng)價(jià)指標(biāo)體系:(4)建立在線評(píng)論質(zhì)量評(píng)價(jià)模型。首先根據(jù)獲得的評(píng)價(jià)指標(biāo)建立在線評(píng)論質(zhì)量評(píng)價(jià)模型,然后將評(píng)論數(shù)據(jù)分為訓(xùn)練集和測(cè)試集,并利用訓(xùn)練集獲得模型中的各評(píng)價(jià)指標(biāo)權(quán)重和利用測(cè)試集驗(yàn)證模型性能。(5)模型性能驗(yàn)證。對(duì)模型的性能驗(yàn)證將從兩方面進(jìn)行:一是利用本文提出的1W2R3C指標(biāo)體系,和WRC與1R3C指標(biāo),分別建模進(jìn)行對(duì)比分析;二是基于本文模型訓(xùn)練的指標(biāo)權(quán)重,引入專家打分法和灰色關(guān)聯(lián)度修正法分別獲得指標(biāo)權(quán)重,然后進(jìn)行建模對(duì)比分析,由此充分驗(yàn)證模型的優(yōu)良性能。本文關(guān)于在線評(píng)論質(zhì)量評(píng)價(jià)模型的研究結(jié)果,可為深入研究在線評(píng)論內(nèi)容提供一些新的方法和理論依據(jù);用于實(shí)踐后也可為廣大消費(fèi)者提供相應(yīng)的決策支持。
[Abstract]:With the rapid development of online shopping market and the variety and convenience of related shopping platforms and applications, online shopping brings great convenience to people's life. More and more people are beginning to accept and choose this way of life. However, because of the virtual and non-touchable nature of online goods, people cannot perceive the quality of products they want to buy in advance. As a result, many people tend to rely on online reviews of goods and make purchase decisions. This has led some unscrupulous businesses to create a large number of reviews through various means, such as "positive reviews". This not only increases the time cost of consumers screening comments, but also may cause unnecessary economic losses. How to quickly identify high quality online reviews has become a new topic in the research of online review content. Based on the content of online comments, this study firstly extracts the characteristic indexes that affect the quality of online reviews. Then the evaluation index system and model of online comment quality are constructed, and the performance of the model is verified. The specific content includes the following five parts: 1) the validity of comment text. By improving the length based automatic tagging algorithm and K-means algorithm, Lk-means algorithm is proposed to annotate the validity of comment text and extract the index of validity. The online comment data can be divided into two categories: numerical and text-type. The integrity index can be obtained by combining the two methods. And extract the score data from the numerical comments, and extract the amount of information from the text comments, readability, According to the WRC index of improving information quality evaluation and the 1R3C index of data quality evaluation found in the research, In this paper, the evaluation index system of 1W2R3C is proposed to establish the online comment quality evaluation model. Firstly, the online comment quality evaluation model is established according to the obtained evaluation index, and then the comment data is divided into training set and test set. The weight of each evaluation index in the model is obtained by using the training set and the model performance is verified by the test set. The performance verification of the model will be carried out from two aspects: one is using the 1W2R3C index system proposed in this paper, and the other is the WRC and 1R3C index. Secondly, based on the index weight of the model training in this paper, the expert scoring method and the grey correlation degree correction method are introduced to obtain the index weight respectively, and then the model is compared and analyzed. The results of this paper can provide some new methods and theoretical basis for further research on online comment content. After being used in practice, it can also provide corresponding decision support for consumers.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F724.6;F274;F713.55

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