博碩士論文 101423004 詳細資訊




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姓名 黃于珊(Yu-Shan Huang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱
(Finding Customer Opinions based on User-given Aspect)
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摘要(中) 由於網際網路的快速發展,許多服務與購物網站累積了許多評論資訊。為了提供給消費者更多資訊使得使用者能更快速找到自己需要的評論,這些電子商務網站會利用事先定義好的產品構面將使用者的評論進行分類。以Hotels.com為例,他們事先將評論分為服務、清潔度、舒適度、整體外觀等四個構面,並給予每個構面分數。然而,依據各網站自我事先的定義,無法針對每位使用者個別的特殊需求,讓使用者找到自己所關心的相關評論資訊。 因此本研究提出新的評論分析方法,能夠利用使用者能動態給予的產品構面找到相關的顧客評論。本研究包含四個主要步驟:前處理(pre-processing)、註解(annotating)、配對(matching)與排列(ranking)。而在註解過程中執行三個不同的方法找到與使用者提供的構面相關的關鍵字。而在配對過程中,利用Google與WordNet進行關鍵字的相似度計算,並利用四個不同的鏈結方法計算構面與句子的相似度。最後利用每個句子與構面的相似度排列評論句子提供給使用者。
摘要(英) As the Internet grows day by day, customer reviews in e-commerce websites are also increasing every day. To provide users more information, these commercial websites usually would summarize users’ opinions in reviews according to some predefined aspects. For example, Hotels.com extract scores from customer reviews in four aspects, including service, cleanliness, comfort and condition. However, the weakness of this predefined analysis approach is that users cannot find opinions on the aspects that have not been considered beforehand by the websites.
To solve this problem, we propose a new approach to generate a ranking of related opinions based on user’s dynamically given aspect. The proposed approach involves 4 phase named pre-processing, annotating, matching and ranking. In the annotating phase, 3 different annotating methods are used to annotated related keywords based on user-given aspect. In the matching phase, Google similarity and WordNet similarity, are first used to compute the similarity between keywords and then the similarity between aspect and each sentences is computed by 5 different linkage methods. Finally, we rank sentences based on their similarities with aspect.
關鍵字(中) ★ 意見探勘
★ NGD
★ WordNet
★ 關鍵字註解
★ 構面分析
關鍵字(英) ★ Opinion Mining
★ NGD
★ WordNet::Similarity
★ Keyword Annotation
★ Aspect Analysis
論文目次 Abstract i
摘要 ii
致謝 iii
List of Figures vi
List of Tables vii
1.Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Our Approach 2
2.Related Work 7
2.1 Opinion Mining 7
2.1.1 Subjectivity analysis 7
2.1.2 Semantic orientation 7
2.1.3 Feature-based opinion mining 8
2.2 Our work 9
3.Research Design 11
3.1 Pre-processing 11
3.2 Annotation 13
3.2.1 Manual method 13
3.2.2 Semi-auto method 13
3.2.2.1 Keyword extraction 14
3.2.3 Fully-auto method 15
3.3 Matching 15
3.3.1 Keywords pair distance identification 16
3.3.2 Aspect-sentence distance identification 17
3.4 Output the ranking of opinions 19
4.Experiment 20
4.1 Datasets 20
4.2 Evaluation methods 20
Then, we can compute accuracy, recall and F-measure in each class. Hence, we can use them to observe the result and draw graph to compare these methods. 21
4.3 The results of experiment 21
4.3.1 The methods of annotation 23
4.3.2 The methods of computing keyword-keyword pair distance 25
4.3.3 The methods of computing aspect-sentence pair distance 26
4.4 The best recommended methods 28
5. Conclusion and Future Works 32
6. Reference 33
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指導教授 陳彥良(Yen-Liang Chen) 審核日期 2014-7-14
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