臺灣博碩士論文加值系統

電子商務商店的快速增長導致越來越多的論壇可供客戶於線上分享對零售商的意見(例如：amazon.com、walmart.com、nike.com、以及levi.com)以協助他們的購買決策過程，同樣的效果也見於產品的第三方評論(例如：epinions.com、rateitall.com、以及ZDNET.com)。透過可讓客戶表達其經歷以及寫下評價評論，這樣的論壇對客戶做出購買決策有很大的幫助，而且可幫助公司擬定促銷和巿場經營策略。然而，由於每天發布的評論量大，導致信息過載使得消費者尋找可靠的訊息以做出購買決策的投入變得複雜。最近許多研究聚焦於以使用者在社群討論區對產品或服務的評價分數等級之累計投票來確定評論者在線上對產品或服務評價之可信度。然而，傳統的評估方法僅考慮投票分數而未考慮隨著訊息來源及評論內容品質之評論者的專業知識和行為，因此使用者用戶驅動的方法具有偏差限制、低覆蓋率和有限的適用性。
因此，為了尋找可靠訊息，本研究旨在提出兩種基於社會機制的新方法，包括信任和聲譽。第一個提出的方法是社群信任的設計示例，藉由挖掘使用行動電話的開箱文說明案例，來構建可靠的共同創建之推薦模型(TCo-CR)。並透過實證實驗以李克特七點量表檢驗研究參與者的滿意度以評估此模型。第二種方法是根據評論者在不同產品類型的評論，基於他們在線上意見分享論壇上發布評論的語言風格之聲譽檢驗來定位信譽良好的評論者(L2R2)。並以實驗和搜索產品類型藉由邏輯斯廻歸和支援向量機(SVM)方法對所提出的L2R2模型的功能進行評估。
本研究的兩種方法都在實際數據集上完全實施和測試，然後與基線模型進行比較。因此，所提出的模型優於基線模型，在評估網路評論者的聲譽時具有更高的客戶滿意度和更高的估計準確性。除了提出了識別可靠訊息和信譽良好的網路評論者的新方法之外，所提出的方法不僅可讓客戶有效地定位他們期望的產品和服務，也對行銷人員有效獲得可靠訊息以促銷和開發產品與服務，和客戶及信譽良好的評論者評論共同創造價值有很大貢獻。

The rapid growth of e-commerce stores has led to an increasing number of online forums that allow customers to share their opinions (e.g., amazon.com, walmart.com, nike.com, and levi.com) regarding the retailers in order to facilitate their purchase decision-making process, as well as third-party product reviews (e.g., epinions.com, rateitall.com, and ZDNET.com). By allowing customers to express their experiences and rate reviews written by others, such forums greatly aid both customers in making purchase decisions and also companies in conducting promotion and marketing strategies. However, information overload due to the huge amount of reviews posted daily complicates the efforts of consumers to locate reliable information when making a purchase decision. Numerous recent studies have focused on identifying credible online reviewers of products or services by basing their rating scores on accumulated votes from the community of users. However, conventional evaluation methods consider only voting scores and fail to consider the reviewers’ expertise and behavior with respect to the source of information and the content quality of the reviews, and thus the user-driven approach has bias limitations, low coverage, and limited applicability.
Therefore, regarding to find reliable information, this study aims to propose two novel approaches based on social mechanism including trust and reputation. The first proposed approach is in term of trust in society designed using an illustrative example of mobile phones to build a trustworthy co-created recommendation model (TCo-CR) by mining unboxing forums. The model is evaluated via an empirical experiment to examine the satisfaction of study participants by using a seven-point Likert scale. The second approach is in term of reviewers’ reputation tested on different product types to locating reputable reviewer method (L2R2) based on their linguistics styles of reviews posted on online opinion sharing forums. The performances of the proposed L2R2 model are evaluated on experience and search product types using logistic regression and the Support Vector Machine (SVM) methods.
Both approaches of this study are fully implemented and tested on real-world datasets then compared with the baseline models. As the result, the proposed models outperform the baseline models and have greater customers’ satisfaction and higher estimation accuracy in evaluating the reputations of online reviewers. In addition to providing novel approaches to identifying trustable information and reputable online reviewers, the proposed approaches not only allows customers to locate their desired products and services efficiently, but also significantly contributes to the efforts of marketers in promoting and developing products and services based on trustable information by co-creating with users and reputable reviewer comments.

摘要 I
Abstract III
Acknowledgement V
Content VI
List of Figures VIII
List of Tables IX
Chapter 1. Introduction 1
1.1 Research background and motivation 1
1.2 Research objective 4
1.3 Research procedure 4
Chapter 2. Related Works 7
2.1 Co-creating value with consumers 7
2.2 Customer reviews and sentiment analysis 8
2.3 Trust-based recommendation models 9
2.4 Linguistics styles of reviews 10
Chapter 3. Research Methodology 15
3.1 Research framework for case 1 15
3.1.1 Data preprocessing 15
3.1.2 Determining feature preferences 16
3.1.3 Sentiment analysis 16
3.1.4 Trust scoring 18
3.1.5 Recommendation aggregation 20
3.2 Research framework for case 2 24
3.2.1 Web crawling 25
3.2.2 Data preprocessing 25
3.2.3 Extracting extra-propositional data 26
3.2.4 Identifying reputable reviewers 31
3.2.5 Performance measures 33
Chapter 4. Experiment and Analysis 35
4.1 Experiment and analysis for case 1 35
4.1.1 Data preprocessing 35
4.1.2 Experimental evaluations 45
4.2 Experiment and analysis for case 2 53
4.2.1 Data collection and data preprocessing 54
4.2.2 Experimental evaluations 56
Chapter 5. Conclusions and future work 67
5.1 Conclusions 67
5.2 Theoretical and managerial contributions 69
5.3 Limitations and directions for future research 70
References 73

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