【摘要】：品牌形象挖掘是了解品牌形象的關(guān)鍵步驟,是塑造提升品牌形象、制定品牌傳播策略的第一步,對品牌形象構(gòu)建以及品牌競爭都具有深遠(yuǎn)的意義。隨著互聯(lián)網(wǎng)技術(shù)的高速發(fā)展,用戶隨時隨地在接觸和獲取信息,并創(chuàng)建了大量的用戶生成內(nèi)容。品牌傳播被置身于更加靈敏便捷、自由高速卻又充滿不確定性的傳播空間中。傳統(tǒng)的品牌形象調(diào)查,在樣本的多樣性和時效性、以及分析方法上已不能滿足品牌形象挖掘的需求。在這種環(huán)境下,海量碎片化的用戶生成話題數(shù)據(jù)為品牌形象挖掘提供了豐富可行的數(shù)據(jù)資源和新的研究思路。基于用戶生成的品牌相關(guān)話題挖掘品牌形象,理解品牌在用戶心中形成的認(rèn)知和情感共鳴,是新傳播環(huán)境下品牌形象傳播的戰(zhàn)略基礎(chǔ)。用戶形成的品牌形象包括用戶對品牌的認(rèn)知、情感、行為三個方面,本文只關(guān)注認(rèn)知和情感兩個維度。首先給出面向品牌形象挖掘的話題識別方法,從而針對不同的分析訴求,對所需的數(shù)據(jù)范圍進行取舍。其次,設(shè)計面向品牌形象認(rèn)知和情感的挖掘方法,從海量、碎片化的用戶生成話題數(shù)據(jù)中獲取用戶對品牌形象的認(rèn)知和用戶對品牌形象的個體情感以及群體情感狀態(tài)。論文的具體研究內(nèi)容如下:(1)面向品牌形象挖掘的話題識別方法。品牌形象挖掘使用的數(shù)據(jù)范圍因任務(wù)側(cè)重點不同而不同。論文面向品牌形象挖掘給出不同的話題識別方法,首先基于關(guān)鍵詞搜索品牌相關(guān)的常規(guī)話題,給出品牌關(guān)注度時序曲線,企業(yè)可以根據(jù)關(guān)注度差異選取數(shù)據(jù)范圍。其次,基于曲線分類建模的思想預(yù)測熱門話題,適應(yīng)高時效性要求的品牌形象挖掘任務(wù)。熱門話題發(fā)現(xiàn)的基本思想是從話題的統(tǒng)計特性出發(fā),使用傳播擴散程度和關(guān)注聚焦程度刻畫話題的熱度,建立話題的熱度曲線。通過對話題的熱度曲線進行預(yù)處理,消除原始量綱對熱度曲線內(nèi)在相似性判定帶來的負(fù)面影響。并對豐富多變的曲線進行分類建模,從中提取共性特征和行為規(guī)律,使之呈現(xiàn)出較為明朗的規(guī)律性。應(yīng)用熱度曲線分類模型上的加權(quán)投票規(guī)則預(yù)測新話題是否會發(fā)展成熱門話題�；陉P(guān)鍵詞搜索常規(guī)話題和基于曲線分類建模識別熱門話題可以滿足品牌形象挖掘?qū)?shù)據(jù)選擇的一般要求。(2)基于話題的品牌形象認(rèn)知分析方法。用戶對品牌形象的認(rèn)知指的是用戶對品牌的整體印象(包括功能、服務(wù)、效用等的評價),是品牌形象傳播的基礎(chǔ)。論文提出一種基于規(guī)則的認(rèn)知標(biāo)簽提取方法,從用戶生成內(nèi)容中掌握用戶對品牌形象的認(rèn)知。首先,基于語言規(guī)則提取出初始的認(rèn)知標(biāo)簽;然后,借助于同義詞詞典和Jaccard相似度對認(rèn)知標(biāo)簽進行聚合;最后,應(yīng)用TFMF模型計算聚合后不同認(rèn)知標(biāo)簽的重要性。根據(jù)所獲取的重要認(rèn)知標(biāo)簽,企業(yè)能夠更好的理解消費者對品牌的整體印象、最在意的品牌特性以及與競品相比品牌所擁有的獨特屬性。(3)基于話題的品牌形象個體情感分析方法。古語有云:“攻心為上”。情感是品牌傳播的攻心武器,對品牌形象挖掘離不開對用戶個體情感狀態(tài)的把握。有效提取用戶生成內(nèi)容的情感標(biāo)簽是品牌形象個體情感分析的基礎(chǔ)。新詞的涌現(xiàn)、熱詞的漂移、海量碎片化及中文常用詞特性帶來的高維稀疏性成為中文情感分類的主要困難。論文提出一種新穎的方法用以解決上述問題:構(gòu)造表情符號詞典用來自動獲取訓(xùn)練集情感標(biāo)簽,解決海量數(shù)據(jù)的標(biāo)注問題。這樣可以節(jié)省訓(xùn)練標(biāo)簽所需的人力和財力成本,且具有較高的客觀性。引入修正的G2檢驗聯(lián)合情感詞詞典進行特征選擇,該方法可以保留強分類能力的特征而不至于過過濾,并盡可能消除無效特征的干擾,從而進行降維,控制稀疏性。采用多階段判斷式的抽樣策略生成訓(xùn)練集,保證基分類器的多樣性。最后采用加權(quán)多數(shù)投票的方式對基分類器結(jié)果進行融合,解決特征和情感漂移及碎片化問題。實驗表明該方法可以快速有效的獲取訓(xùn)練標(biāo)簽,保留下強區(qū)分能力的特征,并實現(xiàn)較高的精度。且該方法很容易擴展到流數(shù)據(jù)并實現(xiàn)并行化。(4)基于話題的品牌形象群體情感分析方法。情感作為消費體驗中最重要的角色,理解用戶對品牌的群體情感狀態(tài)以及群體情感演化邏輯可以幫助企業(yè)和用戶理解品牌形象。本章構(gòu)造品牌群體情感計量模型,基于個體情感對群體情感進行集結(jié)。建立不同粒度下群體情感時間序列數(shù)據(jù),通過對群體情感時序數(shù)據(jù)的分析,理解品牌群體情感演化的邏輯。分析熱門話題的屬性,了解熱門話題對品牌群體情感演化的影響。通過實驗以及案例分析可以發(fā)現(xiàn),品牌生命周期的不同階段會帶來品牌群體情感的不同狀態(tài)。熱門話題會加速情感演化的過程,且熱門話題的不同屬性會影響群體情感演化的方向,而企業(yè)處理策略會加深或者消解熱門話題帶來的情感演化影響程度。品牌形象挖掘是品牌形象傳播的重要基礎(chǔ)。論文從品牌形象挖掘的數(shù)據(jù)準(zhǔn)備,品牌形象認(rèn)知的挖掘方法、品牌形象情感的挖掘方法三個方面開展研究,幫助企業(yè)從海量碎片化的數(shù)據(jù)中提取品牌形象,理解用戶對品牌形象的感知,進而構(gòu)建品牌的核心競爭力。
[Abstract]:Brand image mining is a key step in understanding the brand image. It is the first step to build up the brand image and develop the brand communication strategy. It is of far-reaching significance to the brand image construction and the brand competition. With the high-speed development of the Internet technology, users contact and obtain information at any time and any place, and a large number of user-generated content is created. The spread of the brand is in a more sensitive, convenient, free, high-speed and uncertain propagation space. The traditional brand image survey, in the diversity and timeliness of the samples, and the analysis method cannot meet the requirement of brand image mining. In this environment, the user-generated topic data of massive defragmentation provides rich and feasible data resources and new research ideas for brand image mining. Based on the user-generated brand-related topic mining brand image, the understanding of the brand's cognitive and emotional resonance in the user's heart is the strategic foundation of the brand image transmission in the new communication environment. The brand image formed by the user includes three aspects of the user's cognition, emotion and behavior of the brand, and the article only concerns the two dimensions of cognition and emotion. Firstly, the topic identification method for brand image mining is given, so that the required data range is selected for different analysis demands. Secondly, the design of the method for the recognition and the emotion of the brand image is designed, and the user's perception of the brand image and the individual emotion and the group emotional state of the user on the brand image are obtained from the mass and fragmented user-generated topic data. The specific content of the thesis is as follows: (1) The topic identification method for brand image mining. The data range used for brand image mining is different from the task focus. In this paper, different topic identification methods are given for brand image mining. First, based on the general topic related to the keyword search brand, the time series curve of the brand attention is given, and the enterprise can select the data range according to the difference of the degree of attention. Secondly, the concept of model based on curve classification is a hot topic, which is suitable for the task of brand image mining with high timeliness. The basic idea of the hot topic discovery is to set up the heat curve of the topic based on the statistical character of the topic, using the degree of spread and the degree of focus and the degree of focus. By pre-processing the heat curve of the topic, the negative effect of the original dimension on the similarity determination of the heat curve is eliminated. And the rich and changeable curve is classified and modeled, and the common characteristic and the behavior rule are extracted from the curve, so that the characteristic is more clear. It is a hot topic to predict whether a new topic can be developed by using the weighted voting rule on the heat curve classification model. The general requirements of brand image mining on data selection can be met based on key word search routine and curve-based classification modeling. (2) The cognitive analysis method of brand image based on the topic. The user's perception of the brand image refers to the user's overall impression of the brand (including the evaluation of function, service, utility, etc.), which is the basis for the transmission of the brand image. In this paper, a rule-based method for extracting a cognitive tag is proposed, and the user's perception of the brand image is grasped from the user-generated content. First, the initial cognitive tag is extracted based on the language rule; then, the cognitive tag is aggregated with the aid of the synonym dictionary and the Jaccard similarity; and finally, the importance of different cognitive labels after the aggregation is calculated by using the TFMF model. According to the important cognitive label acquired, the enterprise can better understand the overall impression of the brand by the consumer, the brand characteristic which is the most important, and the unique property owned by the brand as compared with the competitor. (3) The individual emotion analysis method of the brand image based on the topic. The ancient language has the cloud: the "to attack one's heart". The emotion is the core weapon of the communication of the brand, and it is necessary to grasp the individual emotion state of the user without the brand image mining. The emotional label that effectively extracts the user-generated content is the basis of the individual emotion analysis of the brand image. The emergence of new words, the drift of hot words, the massive defragmentation and the high-dimensional sparsity brought by the characteristics of Chinese language often become the main difficulty of Chinese sentiment classification. In this paper, a novel method is proposed to solve the above problems: the structure of the emoticon dictionary is used to automatically acquire the emotional label of the training set and solve the problem of the labeling of the mass data. So that the labor and financial cost required by the training label can be saved, and the method has higher objectivity. A modified G2-test combined affective word dictionary is introduced for feature selection. The method can retain the characteristics of strong classification capability without filtering, and eliminate the interference of the invalid features as much as possible, thereby reducing the dimension and controlling the sparsity. And a multi-stage judgment-type sampling strategy is adopted to generate a training set, so that the diversity of the base classifier is guaranteed. And finally, the basis classifier results are fused in a weighted majority vote mode to solve the problems of characteristic and emotion drift and fragmentation. The experiment shows that the method can quickly and effectively obtain the training label, keep the characteristic of the strong distinguishing ability, and realize the higher precision. And the method is easy to extend to the stream data and realize the parallelization. (4) The emotion analysis method of the brand image group based on the topic. Affective as the most important role in the consumption experience, it is to be understood that the user's emotional state of the group and the logic of the group's emotional evolution can help enterprises and users to understand the brand image. This chapter constructs the emotional measurement model of the brand group, and builds the group's emotion based on the individual emotion. The data of the group's emotional time series under different granularities is established, and the logic of the emotional evolution of the brand group is understood through the analysis of the group's emotional time series data. The paper analyzes the properties of the hot topic and the influence of the hot topic on the emotional evolution of the brand group. Through the experiment and case analysis, it can be found that the different phases of the brand life cycle can bring different states of the brand group's emotion. The hot topic can accelerate the process of the emotional evolution, and the different attributes of the hot topics affect the direction of the group's emotional evolution, and the enterprise's processing strategy will deepen or eliminate the influence of the emotional evolution brought by the hot topic. Brand image mining is an important basis for the transmission of brand image. From the data preparation of brand image mining, the mining method of brand image cognition and the mining method of brand image emotion, this paper helps enterprises to extract the brand image from the data of massive defragmentation, and to understand the user's perception of the brand image. And then building the core competitiveness of the brand.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：F273.2

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