發(fā)布時(shí)間：2018-12-20 10:32

【摘要】：隨著通信服務(wù)發(fā)展的深入,出現(xiàn)了許多新問題和新矛盾,這些問題最終都會轉(zhuǎn)化為客戶投訴到達(dá)投訴分析部門。能夠有效地分析投訴,快速的抽取出用戶、分析部門或其他業(yè)務(wù)部門關(guān)注的投訴信息,無疑對正確的處理投訴、快速的發(fā)現(xiàn)問題十分有幫助。然而目前還沒有令人十分滿意的以用戶關(guān)注為中心的投訴信息識別技術(shù),多數(shù)情況下為了保證準(zhǔn)確,仍然只能依靠人工識別,不僅花費(fèi)大量的人力成本和時(shí)間成本,而且也無法較好的適應(yīng)數(shù)據(jù)量大、數(shù)據(jù)量增長速度快的情況,容易造成數(shù)據(jù)的積壓。更重要的是,容易錯(cuò)失及早發(fā)現(xiàn)問題的良機(jī),使投訴信息反應(yīng)的問題無法在進(jìn)一步擴(kuò)大之前得到遏制。這樣不僅會降低客戶的滿意度,嚴(yán)重時(shí)甚至?xí)箍蛻魧ζ放菩蜗髥适判摹?針對以上問題,本課題通過對投訴數(shù)據(jù)進(jìn)行分析,并對投訴分析人員篩選目標(biāo)投訴的行為進(jìn)行研究和記錄,力圖使用技術(shù)手段解決以投訴分析人員的關(guān)注為核心的投訴信息自動(dòng)分類和推送的問題。同時(shí)結(jié)合實(shí)際的項(xiàng)目需求,將其應(yīng)用到投訴信息自動(dòng)分類與推送系統(tǒng)中,為投訴分析人員提供一個(gè)篩選目標(biāo)數(shù)據(jù)的輔助平臺。 本文主要完成以下工作： 一、通過對投訴數(shù)據(jù)和用戶分析習(xí)慣的研究,明確本文所提的自動(dòng)分類和推送的定義和相關(guān)概念。 二、提出了一種基于TF/IDF算法的文本特征抽取方法,并針對正向文本的文本特征,利用關(guān)聯(lián)規(guī)則算法挖掘類特征詞集。 三、提出了一種根據(jù)VSM模型和全部類別類特征詞集構(gòu)筑投訴空間的方法。在此基礎(chǔ)上,提出了由投訴空間、分類算法和分類參數(shù)共同組成的分類模型。 四、通過記錄用戶標(biāo)注樣本的行為,抽取推送關(guān)系映射,并在此基礎(chǔ)上構(gòu)建自動(dòng)推送模型,以達(dá)到正確推送分類結(jié)果到對應(yīng)用戶的目的。
[Abstract]:With the development of communication service, there are many new problems and contradictions, which will eventually turn into customer complaints to the complaint analysis department. Can effectively analyze complaints, quickly extract users, analysis departments or other business departments concerned about complaints information, no doubt for the correct handling of complaints, the rapid discovery of problems is very helpful. However, at present, there is no very satisfactory complaint information identification technology centered on the attention of users. In most cases, in order to ensure accuracy, we still have to rely on manual identification, which not only costs a lot of manpower and time, but also costs a lot of time. And also can not adapt to the large amount of data, the rapid growth rate of data, easy to cause the backlog of data. More importantly, it is easy to miss opportunities for early detection of problems, so that the problem of response to complaints information cannot be curbed before it can be further expanded. This will not only reduce customer satisfaction, and even make customers lose confidence in brand image. In view of the above problems, this subject analyzes the complaint data, and researches and records the behavior of the complaint analysts to screen the target complaints. This paper tries to solve the problem of automatic classification and push of complaint information with the attention of complaint analysts. At the same time, it is applied to the automatic classification and push system of complaint information according to the actual project requirements, which provides an auxiliary platform to screen the target data for the complaint analysts. The main work of this paper is as follows: first, through the research of complaint data and user analysis habits, the definition and related concepts of automatic classification and push are clarified. Secondly, a text feature extraction method based on TF/IDF algorithm is proposed. Thirdly, a method of constructing complaint space based on VSM model and all kinds of feature word sets is proposed. On this basis, a classification model composed of complaint space, classification algorithm and classification parameters is proposed. Fourthly, by recording the behavior of the user tagging samples, extracting the push relation mapping, and constructing the automatic push model on this basis, to achieve the purpose of correctly pushing the classification results to the corresponding users.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.1

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