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研究生:吳珮菁
研究生(外文):Pei-ChingWu
論文名稱:意見探勘分析顧客行為之研究
論文名稱(外文):Customer Behavior Analysis by Opinion Mining
指導教授:李昇暾李昇暾引用關係
指導教授(外文):Sheng-Tun Li
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:59
中文關鍵詞:意見探勘正規概念分析社群網絡分析主題模型
外文關鍵詞:opinion miningformal concept analysissocial network analysistopic model
相關次數:
  • 被引用被引用:1
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  • 下載下載:44
  • 收藏至我的研究室書目清單書目收藏:2
近年來由於Web2.0以及電子商務的盛行,現代人經由線上或是實體店面購物後,常會於購物網站或是自己的部落格中分享購買經驗以及對於產品的評論。潛在消費者可藉由閱讀相關評論來協助購買決策,對於產品製造商而言,相較於傳統的問卷統計、訪談方式,透過分析顧客的評論將可快速了解顧客的喜好以及對於產品特徵值的評價。然而大量的評論使得資訊負載度提高,增加潛在消費者以及製造商搜集資訊的困難度,為了解決上述的問題,近年來有許多研究在進行意見探勘,主要可分為特徵意見探勘以及比較性意見探勘兩種研究方向,其主要分析為產品於各項特徵值表現,但無法提供相關的產品特色給使用者,另一方面,目前電子商務網站為了增加顧客的滿意度,會運用協同過濾的方法分析歷史交易記錄,來進行個人化的推薦,然而購買記錄並不能真實的反應顧客是否滿意產品的資訊,因此,為了更有效地掌握顧客的滿意度以及感受,本研究希望以從顧客回饋分享的評論中萃取出珍貴的顧客價值,建立社群主題模型,此模型主要結合意見探勘的技術以及社群網絡的互動行為,本研究將從中探勘出產品的共同特徵,希望能有效地推薦對的產品給顧客並提供產品的主要特色資訊以協助決策。
為了驗證本模型之績效,本研究於Mobile01平台擷取手機相關評論作為驗證對象,實驗結果顯示 藉由分析評論確實可幫助了解到顧客真實喜好以及對於產品需求的特性,並且應用於產品推薦上可獲得較佳的成效。
In recent years, due to the emergence and increasing development of Web 2.0 and e-commerce sites, customer purchasing behavior has been changed dramatically. Customers tend to log in a forum, blogs or e-commerce sites to share their purchasing experiences which can assist potential consumers in making purchasing decisions. However, with a huge amount of product reviews posted increasingly every day, customers are confused to figure out the most useful and suitable information. In order to solve this problem, there are increasing trends in opinion mining research which divided into two kinds of methods such as feature opinion mining and comparative opinion mining. However, the recent studies have mostly focused on providing users summarized scores to understand sentiment-oriented of product features. In addition, the current methods have only taken into account the comparing the features of two products, but it cannot provide distinctive characteristics of each product, and find related products and customers. On the other hand, the collaboration filtering approach has been considered as a widely used method in e-commerce websites to recommend users personalized products based on the historical transaction. But the transactions could not truly express the buyers’ behavior towards each product. Therefore, in order to more effectively investigate customer's satisfaction and feelings. We proposed a community-topic model by combining opinion mining and relationships of users to predict customer behavior to other products.
Finally, we used data set of customer review from Mobile01 website to evaluate the recommendation performance. The result shows that using the reviews really can mine the customer behavior, and effectively improve the recommendation.
摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 IX
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究步驟與流程 3
第二章 文獻探討 4
第一節 資訊擷取 4
一、 斷詞與詞性標注 4
二、 特徵擷取 4
第二節 意見探勘 6
第三節 正規概念分析 8
一、 正規情境 8
二、 正規概念 9
三、 概念網路 10
四、 模糊正規概念分析 11
第四節 社群網絡分析 13
一、 網絡集中度 13
二、 子群集辨識 14
三、 資訊守門人 15
第五節 主題模型 16
一、 機率式潛藏語意分析 16
二、 潛藏狄利克里分配 18
第六節 模糊邏輯 19
一、 模糊集合 19
二、 模糊運算子 20
三、 α-截集 21
四、 模糊相似度 21
第七節 推薦系統 22
第三章 研究方法 24
第一節 研究流程圖 24
第二節 資料前處理 25
第三節 情緒分析 27
第四節 社群網路偵測 28
一、 模糊正規情境 28
二、 模糊概念網路 29
三、 社群網絡建構 31
四、 子群集間資訊守門人辨識 36
第五節 主題模型建構 38
第六節 社群主題模型建構 40
第七節 推薦系統 42
第四章 實驗與分析 43
第一節 實驗方法 43
第二節 情緒分析 45
第三節 社群偵側 46
第四節 主題模型建構 50
第五節 社群主題模型建構 51
第六節 實驗結果 52
第五章 結論與未來展望 54
第一節 結論 54
第二節 未來展望 55
參考文獻 56
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