近年來隨著網絡的快速發展，接連帶動社群平台的興起，並使得許多消費者樂意在社群網站上留下足跡，提供對於不同品牌真實的反饋。對於企業而言，獲得不同消費者的所傳達出的真實感受能夠幫助管理者了解消費者是如何看待他們的以及企業目前所面臨的問題。過去大量對於個性的研究都集中在人類個性及品牌個性的預測及評估，但都是以量化研究的形式進行。有鑑於此，從社群平台上的評論內容獲得品牌個性是我們所要嘗試的方法，透過文字探勘技術分析企業所傳達的品牌個性是否真正使消費者感知到。
本研究的目標為探討企業所傳達的品牌個性與消費者感知的品牌個性是否呈現一致性，並且根據結果推測是甚麼原因導致不一致性的情況發生。論文中所使用的方法為透過BERT基礎模型(BERT based model)根據我們所要進行的下游任務-多標籤分類進行微調，接著對文中30間企業的實驗資料進行自動標籤。為了使結果更穩健，使用階層式分群法(Hierarchical Clustering)針對企業傳達的資訊進行分群，接著透過主成分分析法(Principal Component Analysis)取出第一主成分作為每一群經由線性組合後的特徵系數，以此系數與BERT自動標籤出來的結果做餘弦相似性檢測(Cosine Similarity)，得出品牌績效指標。實驗結果顯示在這經過分群後的30間企業當中，品牌績效分數最高的為Foot Locker(0.9441)，品牌績效分數最低的為Lands’ End (0.6337)，深入探究後，品牌績效分數較高的企業在企業傳達的品牌個性與消費者所感知的品牌個性上會呈現一致性，而品牌績效分數較低的則會呈現不一致性，企業所要傳達的品牌在傳遞給消費者後，消費者感知到的卻是不同的品牌個性。透過本篇論文的實驗能夠使企業獲得參考依據，幫助企業選擇應該要持續投入在相同的策略上或是應該更換策略去迎合消費者所感知的品牌個性。

In recent years, the rapid development of the internet has led to the rise of social media platforms, and many consumers are willing to leave footprints on social networking sites to provide real feedback on different brands. For corporations, getting the real feelings conveyed by different consumers can help managers understand how consumers perceive them and the problems the corporation is current facing. In the past, number of studies on personality have focused on the prediction and evaluation of human personality, but they are all carried out in a form of quantitative research. In view of this, obtaining the brand personality from the consumer reviews on the social media platforms is the method we want to try. Through the text mining technology, we can analyze whether the brand personality conveyed by the corporation can be perceived by consumers.
The goal of this research is to investigate whether the brand personality conveyed by corporation is alignment with the brand personality perceived by consumers, and to speculate on the reasons for the nonalignment based on the results. The method used in this research is to fine-tune the BERT-based model according to the downstream task – multi-label classification, and then automatically label the experimental data of 30 corporations in this research. In order to make the results more robust, Hierarchical Clustering is used to cluster the information conveyed by corporations, and the first principal component coefficient is extracted by Principal Component Analysis as the coefficient of each cluster after linear transformation. This coefficient and the result of BERT automatic labeling are used for Cosine Similarity detection, and the brand performance index is obtained. The experimental result shows that among the 30 corporations that have been cluster, Foot Locker has the highest brand performance score (0.9441), and Lands’ End has the lowest brand performance score (0.6337). After in-depth research, a corporation with higher brand personality scores will show alignment in the brand personality conveyed by the corporation and the brand personality perceived by consumers, while a corporation with lower brand performance score will show nonalignment, that is to say, after the brand to be conveyed by the corporation is passed to the consumer, the consumer perceives different brand personality. The experiment in this research can help corporate managers understand whether the brand personality they are trying to convey can hit consumers correctly, or whether there will be nonalignment.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
TABLE OF CONTENTS vi
LIST OF FIGURES viii
LIST OF TABLES ix
CHAPTER 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Purpose and Goal 4
1.3 Organization of the Research 4
CHAPTER 2 Literature Review 6
2.1 Brand Personality 6
2.1.1 The Concept of Brand Personality 6
2.1.2 The Relationship between Human Personality and Brand Personality 8
2.1.3 Measuring Brand Personality 14
2.2 Brand Performance 17
2.2.1 The Concept of Brand Performance 17
2.2.2 Measuring Brand Performance 19
2.2.3 The Impact of Brand Personality in Brand Performance 21
CHAPTER 3 Methodology 24
3.1 Data Collection and Data Processing 25
3.2 Corpus Preparation for Labeling Task 29
3.3 BERT-Based Multi-label Text Classification 33
3.4 Measuring Brand Performance 36
CHAPTER 4 Result 38
4.1 Description of the Dataset 38
4.2 Fine-tuned BERT with Evaluation 44
4.3 Clustering Result 48
4.4 Principal Component Analysis and Brand Performance Result 51
4.5 Managerial Implication 55
CHAPTER 5 Conclusion 65
5.1 Conclusion Remarks 65
5.2 Limitations and Future Work 66
References 69

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