跨領域分辨真假評論之研究－以BERT為基礎模型

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姓名

陳莉茿(LI-JU CHEN) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

跨領域分辨真假評論之研究－以BERT為基礎模型
(Identify Deceptive Reviews in Cross-domain Content with BERT)

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摘要(中)

線上評論在電子商務中具有重要的影響力，消費者越來越仰賴這些評論來做出購買決策，然而，不道德的企業可能散佈假評論以操縱消費者意見，而Ott et al. (2011) [19] 實驗表明，人類識別假評論的準確率僅有57.3%，且對於跨領域的真假評論分類模型，目前尚缺乏對於在不同領域間共享的文本特徵和規則之研究，由於模型過度依賴相同來源的資料，導致同個模型在其它資料集測試時，準確率急遽下降。
因此，本研究提出基於 Bidirectional Encoder Representations from Transformers (BERT) 的模型，利用[MASK]替代評論中出現的該領域特定單詞，克服跨領域之間兩者評論風格差異性過大的問題，在我們的研究中使用來自Ott et al. (2011) [19] 和Li et al. (2014) [33] 在餐廳、旅館、醫生領域之評論，以及本研究額外加入Yelp真實評論做為訓練資料。最後，MASK-BERT於實驗結果中，與Ren & Ji (2017) [25] 為目前研究最佳之結果做比較，在Cross-domain中，F1-score最佳表現為 88.49%；而對於內容差異性較大的醫生領域，在本研究提出遮蔽機制後，Accuracy也提升了15~20%。

摘要(英)

Online reviews play a significant role in e-commerce. Consumer has been more relied on them when making decision in purchasing. However, unethical businesses may spread deceptive reviews to manipulate consumer`s opinion. Research by Ott et al. (2011) [19] showed that humans can only identify fraud reviews with only an accuracy of 57.3%. Besides, recent research face a crucial challenge that the cross-domain classification model is too rely on similar datasets from the same domain, which causes in a sharp decline in accuracy when testing on datasets from different domain. Currently, there is a lack of method on text features or rules to share with different domains.
Hence, our study proposes a model based on Bidirectional Encoder Representations from Transformers (BERT). We suggest replacing domain-specific words in reviews with [MASK] to overcome the significant stylistic differences between cross-domain reviews. Our research utilizes reviews from Ott et al. (2011) [19] and Li et al. (2014) [33] in the domains of restaurants, hotels, and doctors, supplemented with Yelp reviews as real data for training. Finally, we compare the results of MASK-BERT with the state-of-the-art approach by Ren & Ji (2017) [25]. In the cross-domain, particularly in the doctor domain with larger content differences, our proposed masking mechanism leads to a highest accuracy improvement of 15-20%.

關鍵字(中)

★ 跨領域
★ BERT
★ 假評論
★ 虛假偵測
★ 遮蔽資訊

關鍵字(英)

★ cross-domain
★ BERT
★ fraud reviews
★ deception detection
★ masking information

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 viii
第一章緒論 1
1-1 研究背景 1
1-1-1 線上評論影響力 1
1-1-2 假評論來源 1
1-1-3 模型應用於真假評論分類 2
1-2 研究動機 4
1-2-1 假評論標註 4
1-2-2 過往研究結果 4
1-3 研究目的 5
1-4 研究架構 6
第二章文獻探討 7
2-1 BERT應用於跨領域之真假評論分類 7
2-2 跨領域定義 Definition of Cross-domain 10
2-3 演算法應用於跨領域之真假評論分類文獻回顧 11
第三章研究方法 15
3-1 研究流程 15
3-2 BERT 16
3-3 遮蔽機制 MASK mechanism 18
3-4 微調機制 Fine-tuning 21
3-4-1 AE-BERT (Auto-encoder based on BERT) 21
3-4-2 MASK-BERT (MASK mechanism based on BERT) 22
第四章研究實驗 24
4-1 資料蒐集 24
4-2 資料前處理 25
4-2-1 MongoDB 25
4-2-2 特徵生成 26
4-3 超參數 28
4-4 實驗結果與分析 29
4-4-1 損失函數 30
4-4-2 In-domain 31
4-4-3 Cross-domain 33
第五章結論與未來研究之建議 34
5-1 研究結論 34
5-2 研究限制與未來建議 35
第六章參考文獻 36

參考文獻

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2023-7-26

推文