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系統識別號 U0026-0408201622524200
論文名稱(中文) 線上賣家多面向階層式評論分析方法
論文名稱(英文) Multi-aspect Hierarchical Review Analysis Method for Online Sellers
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 104
學期 2
出版年 105
研究生(中文) 陳鈺玟
研究生(英文) Yu-Wen Chen
學號 r76034129
學位類別 碩士
語文別 中文
論文頁數 57頁
口試委員 口試委員-盧文祥
口試委員-高宏宇
口試委員-劉任修
指導教授-王惠嘉
中文關鍵字 網路拍賣  線上評論  特徵擷取  意見探勘 
英文關鍵字 Online auction  Online reviews  Feature extraction  Opinion mining 
學科別分類
中文摘要 由於電子商務的蓬勃發展,使得網路購物的比例越來越高、網路商店的數量日趨增長,因此消費者在進行購買決策前,往往會考量不同面向以進行商店之間的比較,除了檢視店家本身給予的資訊以外,過去買家所撰寫的線上評論亦是重要的參考資訊。隨著Web 2.0的盛行,除了增進資訊傳播的便利性外,網路評論的成長數量也相當驚人,然而,消費者礙於時間、精力有限,往往無法一一瀏覽每則評論、不易從繁雜的評論資訊中,簡單扼要的針對想關注的細節進行檢視。
過去針對線上評論進行分析的研究中,大多聚焦於透過產品特徵的意見極性來對評論的整體評價進行預測,較少在針對個別特徵進行意見分析後,再將特徵進行不同層面的統整歸納;另外在評論依據的部分,先前研究大多以單次的線上評論來進行特徵與意見字詞的極性分析,較無考慮後續追加評論的影響,因此本研究提出一自動化的分析方法,藉由統整過去買家所撰寫的評論,篩選出相關的特徵與意見字詞,並綜合考量程度字詞、否定字詞與追加評論之時間權重等因素,以對消費者在意的購物面向進行特徵與意見極性的整合分析,以輔助消費者進行購買決策時的參考。實驗結果顯示本研究提出之考慮時間權重的影響力下,對於評論極性識別的正確率為0.852、透過系統自動擷取出的特徵進行購物面向之歸納結果F-measure為0.532。
英文摘要 Due to the growth of E-commerce, online shopping becomes more and more popular. Before consumers do shopping online, they often tend to inspect the opinions written by past buyers. With the development of Web 2.0, it increases not only the convenience of information spread, but the amount of online opinions. However, the explosive growth of online reviews makes it time-consuming for consumers to quickly obtain useful information which they concern.
Besides, many online platforms provide reviews of specific product or store merely, which lack integrating opinions and multi-aspect information such as service or delivery speed for potential consumers. Moreover, although many platforms provide different types of opinion rates for consumers to evaluate their transactions, consumers may not rate once but to give additional comments after utilizing products for a period of time. Nevertheless, previous researches seldom take those opinions, which are closer to the real situation, into consideration.
Hence, this study proposes a multi-aspect analysis method for online sellers and presents the integrated information of stores, helping consumers inspect detailed opinions based on their needs. The experimental results show that this study can identify opinion polarity changing with Accuracy 0.852 and feature categorizing with F-measure 0.532.
論文目次 第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 4
1.4 研究流程 4
1.5 論文大綱 5
第2章 文獻探討 7
2.1 自然語言處理 7
2.1.1 向量空間模型 7
2.1.2 中文斷詞處理 8
2.2 特徵擷取 10
2.3 意見探勘 13
2.3.1 意見極性判斷 13
2.4 網路購物選擇評估面向 15
2.5 小結 18
第3章 研究方法 19
3.1 研究架構 19
3.2 資料前處理模組 21
3.3 樣版規則設計與特徵擷取模組 22
3.4 特徵與意見字詞極性整合模組 25
3.5 面向整合模組 28
3.6 小結 34
第4章 系統建置與驗證 35
4.1 系統環境建置 35
4.2 實驗方法 35
4.2.1 資料來源 36
4.2.2 評估指標 39
4.3 參數設定 40
4.4 實驗結果 42
4.4.1 實驗一 42
4.4.2 實驗二 44
4.4.3 實驗三 45
4.4.4 實驗四 47
第5章 結論與未來方向 49
5.1 研究成果 49
5.2 未來研究方向 52
參考文獻 53
附錄 57
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