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姓名 林佳勳(Jia-Syun Lin)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 以謠言與謊言理論為基礎之真假評論識別研究
(Identifying Deceptive review comments with rumor and lie theories)
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摘要(中) 現今,互聯網普及的時代,人們已習慣頻繁的參與網路上的互動,不僅積極將自身的真實體驗發佈分享,同時也扮演訊息接收者的角色,如此而產生的大量評論已成為多數人購買商品或服務前的參考依據,亦有調查數據顯示,消費者對於線上評論的信任程度為逐年成長的趨勢,其中負向評論更是影響消費者的決策。不幸的是,由於線上評論的快速傳遞特性與巨大影響力,許多組織開始蓄意誇大自身產品或捏造負向評論來攻擊競爭對手,以期從中獲取利益,然而,這樣濫用線上評論的同時,對消費者個人及商業組織皆造成損害。研究顯示,受到網路評論而影響購買意圖最強烈的,即是旅遊業及旅館業,這些評論記錄實用的旅遊資訊及個人經驗,是旅客在出發至陌生景點前的重要參考資訊。
故本研究將以芝加哥前二十間知名旅館的負向真實評論及虛假評論為研究對象,包括在六個知名旅遊評論網站上的真實評論,以及由亞馬遜群眾智慧平台Amazon Mechanical Turk10所蒐集的虛假評論。並以謠言及謊言理論為基礎延伸六大屬性:旅館重要屬性字、模糊字、第一人稱代名詞、負面用詞、簡化思考代名詞以及冗詞贅字。運用文字探勘技術結合分類演算法進行分類器訓練,再利用多個單一分類器預測之結果進行分類器整合研究,建構準確且效率兼備的虛假評論識別模型。本研究建構之分類模型結果顯示,利用六大屬性進行資料維度縮減後,不僅讓運算效率提升,同時保持合理的準確性,有效的進行真實與虛假評論之識別,而分類器整合後所得之精確度(Precision)、召回率(Recall)、準確度(Accuracy)及F值(F-measure)四個指標值,也勝過單一分類器之效能,並能有效避免單一分類器可能不適用於其他資料集之風險。
摘要(英) Nowadays, people got accustomed to participate interaction frequently on the web in the era of internet. They were not only sharing their real-life experience actively , but also playing the role of the recipient of the message, and thus generated a lot of comments had become references for most people buying products or services. The survey data also showed that consumers trusted online reviews growing year by year, in which deceptive reviews had much more influences on consumer decisions. Unfortunately, due to the rapid transfer and enormous influence were typical of online reviews, many organizations began to deliberately exaggerate their own products or fabricated negative comments to attack competitors in order to derive benefit. However, individual consumers and commercial organizations would cause damage by abusing online reviews. Studies had shown that, it had much more influence by online reviews were tourism and hotel industry. These comments recorded of useful tourist information and personal experience which were important information for travelers in unfamiliar places before departing.
This study discussed the negatively truthful review and the deceptive reviews from top twenty famous hotels in Chicago, including the true reviews taking from six famous review sites and the comparison group deceptive reviews on Amazon Mechanical Turk10. On the basis of the rumors and lies theories, the method created six attributes, key words of hotel, vague words、personal pronoun、negative words、pronouns and pleonasm. By using text mining combined classification algorithm to forecast outcome and apply to build models. In this model showed that the mathematical operations not only worked more efficiently but kept the accuracy reasonably, so it could distinguish true or deceptive reviews well. After integrating classifiers, the four indicators for “ Precision”, ”Recall”, “Accuracy”, and “F-measure” had better efficacy than single classifier , also could avoid the risk of unsuitable for other data set.
關鍵字(中) ★ 謠言
★ 謊言
★ 虛假評論
★ 負向評論
★ 文字探勘
關鍵字(英)
論文目次 中文摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 3
1-3 研究目的 7
1-4 研究架構 8
第二章 文獻探討 9
2-1 謠言相關理論 9
2-1-1 謠言的定義 9
2-1-2 謠言發生的動機與原因 9
2-2 謊言(捏造故事)的特性 10
2-3 負向虛假評論 11
2-4 關鍵詞擷取方法 12
2-5 文字探勘 14
2-6 分類器的整合 16
2-6-1 分類器整合原因與方法 16
2-6-2 分類器整合之相關研究 18
2-7 真假評論識別之相關研究 19
第三章 研究方法 21
3-1 研究設計 21
3-2 基於TF-IDF 的判斷模型 22
3-3 資料維度縮減 23
3-4 評估模型建立 25
3-5 整合分類器 26
3-6 實驗結果評估準則 27
第四章 研究實作 29
4-1 資料集 29
4-2 研究結果 30
4-2-1 單一分類器-效能比較 30
4-2-2 單一分類器-效率比較 32
4-2-3 整合分類器實驗結果 32
第五章 結論與建議 37
5-1 研究結論 37
5-2 研究限制及未來研究建議 38
參考文獻 40
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