臺灣博碩士論文加值系統

在市場經濟中網路上的企業占的比重越來越多，導致了網路上的評論有很大影響力和重要性，在購買產品之前，有越來越多的使用者會使用論壇來查詢該產品的意見或是分享使用產品的體驗，然而在現實世界中充滿著許多的假評論，顧客無法很明確的識別非假評論和假評論的區別，在網路上的假評論可能影響著顧客的購買決定，因此本研究提出了一個能識別假評論的模型，在本研究中使用了論壇資料是台灣知名論壇Mobile01，並使用了文字探勘的技術來處理非結構性資料，包含了詞袋模型、潛在語義分析、word2vec。接下來本研究用機器學習來訓練一個能識別假評論的分類器，使用了SVM和使用深度學習的方式包含了深度神經網絡（DNN）、和卷積神經網絡（CNN）、長期短期記憶（LSTM），最後本研究選出三個最佳效能的模型找出疑似是寫手的評論，讓未來想做此研究增加了寫手的樣本數。

The increasing share of the online businesses in market economy has led to a larger influence and importance of the online reviews. Before making a purchase, users are increasingly inclined to browse online forum that are posted to share post-purchase experiences of products and services. However, there are many fake reviews in the real world, consumers can't identify authentic and fake reviews. Fake online shopping reviews are harmful to consumers who might buy misrepresented products. Therefore, we proposed a framework which could detect fake reviews. In this study, we focused on the data on the web forum called Mobile01 and used text mining to deal with textual data including Bag-of-words, Latent Semantic Analysis and word2vec for word representation. Next, we used machine learning to train the model to detect fake review, including SVM, Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Finally, we chose three best performance models to vote and hope that these fake reviews samples can be the reference in the future research.

表目錄 v
圖目錄 vi
1、緒論 1
2、文獻回顧 3
2.1假評論 3
2.2文字探勘 4
2.3文件分類 5
2.4深度學習 6
2.4.1卷積神經網路(CNN, Convolutional Neural Network) 7
2.4.2長短期記憶(LSTM, Long Short-Term Memory) 7
3、研究方法 9
3.1研究架構 9
3.2資料來源 10
3.3資料前處理 11
3.3.1斷詞停用詞去除 11
3.3.2向量表達 14
3.4模型建置 16
3.5模型評估 17
4、實驗設計 19
4.1實驗資料 19
4.2資料切割 19
4.3利用發文的內容建立分類模型 20
4.3.1實驗1：使用BOW與不同分類法結果 21
4.3.2實驗2：使用LSA與不同分類法結果 22
4.3.3實驗3：使用word2vec與不同分類法結果 24
4.3.4小結 25
4.4只使用量化指標 31
4.4.1實驗4：量化指標使用不同分類法結果 32
4.5發文內容結合量化指標 34
4.6使用不同分類器挑出疑似是寫手的文章 36
5、結論與未來研究 38
參考文獻 40

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