臺灣博碩士論文加值系統

隨著Web2.0發展，網路的強大影響力改變了整個社會的溝通模式。使用者利用網路平台所提供的討論機制，例如U-Car、Facebook、Mobile 01、PTT…等平台，讓大家可以提供自身意見與訊息交流。現今許多消費者在購買商品之前會根據網路上的部落格、社群網站、電子商務網站等平台，尋找他們想要購買的商品資訊並做為購買的參考依據。因此，在這樣的新興電子商務環境底下，線上消費者的評論一直扮演著重要的角色。然而，有部分公司企業試圖利用操弄評論來提升銷售額，但消費者面對著因為網路便利所帶來的大量訊息，很難辨別評論其真偽，只能依靠自身經驗來辨識。因此，本研究依文獻探討結果，定義了包含情感語意、產品特徵、專有名詞等15項潛在操弄特徵屬性。並導入這些評論操弄資訊，支持向量機 (Support Vector Machine, SVM)與利用決策樹 (Decision Tree, DT)，試圖提升操弄評論分類績效。另外，本研究使用相關分析(Correlation analysis)、決策樹萃取之規則與植基於倒傳遞類神經網路 (Backpropagation network, BPN)特徵選取法，以辨識本實驗所提出評論操弄特徵之重要度。最後以手機產品操弄評論實例，驗證本研究所提方法之有效性。

Following the development of Web 2.0, the powerful influence of the Internet has changed the communication mechanisms of our society. Internet users used the online communication platforms such as U-car, Facebook, Mobile 01, PTT…etc. to share their opinions and exchange their own experiences. Now, there are lots of consumers depend on the information posted in blogs, social networks, e-business websites and so on to find the information they need before they make the purchase decisions. Therefore, the online consumers’ reviews play an important role in this new e-commerce environment. Consequently, some enterprises attempted to increase the sale volume by manipulating online users’ comments. But, it’s difficult to be identified by consumers. They only can recognize the comments by their own experiences. Therefore, after viewing available literatures, this study defined fifteen features including sentiment, product features, expertise, and so on. By introducing the review manipulation information, we try to improve the classification perforamcnes of Decision Trees (DT) and Support Vector Machines (SVM). In addition, this work used correlation analysis, the extracted rules by decision trees, and neural networks based feature selection method to identify the importance of the defined 15 potential review manipulation attributes. Finally, two real cases of manuplated reviews about smart phones to evaluate the effectiveness of our methods.

第一章 緒論
1.1 研究背景
1.2 研究動機
1.3 研究目的
1.4 研究架構
1.5 實驗步驟
第二章 文獻探討
2.1 網路口碑 (Online word-of-mouth / Electronic word-of-mouth)
2.2 操弄評論 (Review Manipulation)
2.3 機器學習方法
2.3.1 支持向量機 (Support Vector Machines, SVM)
2.3.2 決策樹 (Decision Tree, DT)
第三章 研究方法
3.1 潛在操弄屬性
3.2 實驗流程
第四章 實驗結果與分析
4.1 資料收集與前處理
4.1.1 資料來源與工具
4.1.2 資料前處理
4.2 評估指標
4.3 實驗結果與分析
4.3.1 資料集一結果與分析
4.3.2 資料集二結果與分析
4.3.3 資料集綜合結果分析
第五章 結論與未來研究方向
5.1 研究結論
5.2 未來研究方向
參考文獻
附錄A -資料集一之SVM與DT分類器各實驗結果
附錄B -資料集二之SVM與DT分類器各實驗結果


圖目錄
圖 1-1 同作者帳號進行不同角色回覆-1 ........................................................... 6
圖 1-2 同作者帳號進行不同角色回覆-2 ........................................................... 7
圖 1-3 台灣三星於臉書粉絲團道歉啟事 .......................................................... 8
圖 1-4 研究架構圖 ............................................................................................. 12
圖 2-1 星級評價 ................................................................................................. 28
圖 2-2 SVM 超帄面示意圖 ............................................................................... 34
圖 2-3 決策樹 ..................................................................................................... 35
圖 3-1 實驗流程圖 ............................................................................................. 45
圖 4-1 資料集一使用SVM 分類器實驗之結果 .............................................. 58
圖 4-2 資料集一使用DT 分類器實驗之結果 ................................................. 58
圖 4-3 資料集一使用SVM 分類器實驗之訓練時間 ...................................... 59
圖 4-4 資料集一使用DT 分類器實驗之訓練時間 ......................................... 59
圖 4-5 資料集二使用SVM 分類器實驗之結果 .............................................. 73
圖 4-6 資料集二使用DT 分類器實驗之結果 ................................................. 73
圖 4-7 資料集二使用SVM 分類器實驗之訓練時間 ...................................... 74
圖 4-8 資料集二使用DT 分類器實驗之訓練時間 ......................................... 74


表目錄
表 1-1 潛在操弄評論之範例............................................................................... 5
表 2-1 相關使用研究之文獻............................................................................. 32
表 3-1 美國年級水帄 ......................................................................................... 39
表 3-2 潛在操弄屬性特徵值............................................................................. 41
表 3-3 詞彙文件矩陣範例 ................................................................................. 44
表 4-1 資料來源 ................................................................................................. 47
表 4-2 資料分佈 ................................................................................................. 47
表 4-3 刪除無意義及無效評論之範例 ............................................................ 48
表 4-4 資料集一資料前處理與5 Fold 結果 .................................................... 48
表 4-5 資料集二資料前處理與5 Fold 結果 .................................................... 48
表 4-6 混淆矩陣 ................................................................................................. 49
表 4-7 相關分析係數 ......................................................................................... 51
表 4-8 資料集一使用SVM 分類器實驗之結果 .............................................. 54
表 4-9 資料集一使用DT 分類器實驗之結果 ................................................. 56
表 4-10 資料集一實驗三權重1 比6 之Fold #2 rules 規則 ........................... 62
表 4-11 資料集一實驗三相關分析 ................................................................... 64
表 4-12 資料集一實驗三權重1 比6 之Fold #3 BPN 特徵選取結果 ........... 65
表 4-13 資料集二使用SVM 分類器實驗之結果 ............................................ 67
表 4-14 資料集二使用DT 分類器實驗之結果 ............................................... 70
表 4-15 資料集二實驗三權重1 比2 之Fold #3 rules 規則 ........................... 77
表 4-16 資料集二實驗三權重1 比2 之Fold #5 rules 規則 ........................... 78
表 4-17 資料集二實驗三相關分析 .................................................................. 79
表 4-18 資料集二實驗三權重1 比2 之Fold #3 BPN 特徵選取結果 ........... 80
表 4-19 資料集二實驗三權重1 比2 之Fold #5 BPN 特徵選取結果 ........... 81
表 4-20 決策樹知識規則 ................................................................................... 82
表 4-21 合併BPN 特徵選取所有結果 ............................................................. 83
表 4-22 資料集一、二合併相關分析 .............................................................. 84
表 4-23 相關分析綜合比較表........................................................................... 86
表 4-24 相關分析綜合比較整理表 .................................................................. 87
表 4-25 合併所有資料集分析結果 .................................................................. 88

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