【摘要】：客戶服務(wù)熱線(后簡(jiǎn)稱“客服熱線”)是企業(yè)及時(shí)獲取用戶心聲的重要渠道。長(zhǎng)期以來,囿于技術(shù)手段的局限,客服熱線數(shù)據(jù)分析只針對(duì)話務(wù)量、用戶滿意度等結(jié)構(gòu)化數(shù)據(jù),而對(duì)蘊(yùn)涵有潛在價(jià)值的話音轉(zhuǎn)文本類非結(jié)構(gòu)數(shù)據(jù)挖掘不深。隨著客服熱線話務(wù)量的爆發(fā)式增長(zhǎng),用戶投訴問題類別和范圍的擴(kuò)大,如何從海量熱線文本數(shù)據(jù)中快速識(shí)別投訴主題,研判用戶的投訴情感演化趨勢(shì),成為客服人員亟待解決的重要實(shí)踐問題�？头䶮峋�文本挖掘?qū)儆谝庖娡诰虻难芯糠懂?現(xiàn)有的意見挖掘?qū)ο蠖嘁曰ヂ?lián)網(wǎng)文本數(shù)據(jù)為主,針對(duì)企業(yè)內(nèi)部的客服熱線文本挖掘的研究尚不多見。本文的研究,對(duì)于拓展意見挖掘研究范圍,驗(yàn)證相關(guān)理論、方法的適用性具有較強(qiáng)理論意義。本文以意見挖掘的理論和方法為依托,以R語言為編程工具,對(duì)我國某通信運(yùn)營商2013年9月-2014年9月為期13個(gè)月的客服熱線工單文本信息進(jìn)行語義和情感層面的深度分析,實(shí)現(xiàn)熱線文本主題的自動(dòng)識(shí)別與主題的情感趨勢(shì)預(yù)測(cè)。具體而言,在語義分析層面,應(yīng)用結(jié)構(gòu)化主題建模(Structural Topic Modeling,STM)算法將70余萬條文本記錄自動(dòng)識(shí)別歸類為20個(gè)主題;在情感分析層面,首先,通過構(gòu)建通信領(lǐng)域情感詞庫,設(shè)計(jì)文本情感極性強(qiáng)度算法,總結(jié)熱線文本主題內(nèi)容/情感傾向的分布特征,之后,應(yīng)用時(shí)間序列自回歸分析方法,對(duì)20個(gè)主題的情感傾向趨勢(shì)進(jìn)行預(yù)測(cè),總結(jié)不同類型熱線文本主題情感演化特點(diǎn)。通過以上研究,首先,構(gòu)建了適用于通信行業(yè)客服熱線文本情境的意見挖掘分析框架;其次,分別驗(yàn)證了結(jié)構(gòu)化主題建模算法、文本情感極性強(qiáng)度算法和基于情感極性的文本主題時(shí)間序列自回歸預(yù)測(cè)方法在客服熱線文本語義挖掘和情感挖掘領(lǐng)域的適用性。在實(shí)踐層面,開發(fā)的程序已實(shí)現(xiàn)客服熱線文本主題的自動(dòng)識(shí)別與歸類,文本主題的情感傾向演化趨勢(shì)預(yù)測(cè),拓展了運(yùn)營商客服部門基于熱線文本數(shù)據(jù)決策的新思路。未來的研究可以從分析維度多樣性和分析準(zhǔn)實(shí)時(shí)性兩方面進(jìn)行完善:一方面,考慮將熱線工單的其他“元數(shù)據(jù)”,如將投訴人、投訴地點(diǎn)、問題級(jí)別等因素加入主題模型,豐富語義挖掘多樣性;另一方面,將目前實(shí)現(xiàn)的R單機(jī)程序與Spark等分布式系統(tǒng)結(jié)合,提升分析的準(zhǔn)實(shí)時(shí)性。
[Abstract]:Customer service hotline (customer service hotline for short) is an important channel for enterprises to get users' voice in time. For a long time, due to the limitation of technical means, customer service hotline data analysis is only aimed at structured data such as traffic, user satisfaction, etc., but it is not deep for unstructured data mining of voice transliteration which contains potential value. With the explosive growth of customer service hotline traffic and the expansion of the category and scope of user complaints, how to quickly identify complaint topics from mass hotline text data and study the emotional evolution trend of users' complaints. Customer service personnel become an important practical problem to be solved. Customer service hotline text mining belongs to the research category of opinion mining. Most of the existing opinion mining objects are mainly Internet text data, but the research on customer service hotline text mining in enterprises is still rare. The research in this paper is of great theoretical significance for expanding the research scope of opinion mining and verifying the applicability of relevant theories and methods. Based on the theory and method of opinion mining and using R language as programming tool, this paper makes a deep semantic and emotional analysis on the text information of a customer service hotline from September 2013 to September 2014. The automatic identification of hot line text topic and the prediction of emotional trend are realized. Specifically, at the level of semantic analysis, using the structural topic Modeling (STM) algorithm, more than 700,000 text records are automatically classified into 20 topics. After designing text affective polarity intensity algorithm and summarizing the distribution characteristics of hot-line text topic content / affective tendency, using time series autoregressive analysis method to predict the tendency of emotional tendency of 20 themes. This paper summarizes the characteristics of emotional evolution of different types of hot wire text. Through the above research, firstly, we construct a framework of opinion mining analysis suitable for the text situation of customer service hotline in the communication industry. Secondly, we verify the structured topic modeling algorithm, respectively. The applicability of text affective polarity intensity algorithm and text subject time series autoregressive prediction method based on affective polarity in the field of customer service hotline text semantic mining and emotional mining. On the practical level, the developed program has realized the automatic identification and classification of the customer service hotline text topic, and the prediction of the trend of emotional tendency evolution of the text theme, which has expanded the new thinking of the operator customer service department based on the hot line text data decision-making. Future research can be improved in terms of dimension diversity and quasi-real-time analysis: on the one hand, consider adding other "metadata" of hotline work order, such as the complainant, complaint location, problem level and other factors into the thematic model. On the other hand, the realization of R single program is combined with distributed systems such as Spark to improve the quasi-real-time performance of analysis.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.1;F626

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本文編號(hào)：2134103