ChatGPT-Driven Fake Review Detection: Assessing the Impact of Review Aspects

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：47

、訪客IP：52.15.187.50

姓名

林介元(CHIEH-YUAN LIN) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(ChatGPT-Driven Fake Review Detection: Assessing the Impact of Review Aspects)

相關論文

★ 不動產仲介業銷售住宅類別之成交預測模型—以不動產仲介S公司為例	★ 應用文字探勘技術建構預測客訴問題類別機器學習模型
★ 以機器學習技術建構顧客回購率預測模型：以某手工皂原料電子商務網站為例	★ 以機器學習建構股價預測模型：以台灣股市為例
★ 以機器學習方法建構財務危機之預測模型：以台灣上市櫃公司為例	★ 運用資料探勘技術於股票填息之預測模型：以台灣股市上市公司為例
★ 運用資料探勘技術優化次世代防火牆規則之研究	★ 應用資料探勘技術於電子病歷文本中識別相關新資訊
★ 應用深度學習於藥品後市場監督：Twitter文本分類任務	★ 運用電子病歷與資料探勘技術建構腦中風病人心房顫動預測模型
★ 考量特徵選取與隨機森林之遺漏值填補技術	★ 電子病歷縮寫消歧與一對多分類任務
★ 運用Meta-path與注意力機制改善個人化穿搭推薦	★ 運用機器學習技術建構核保風險預測模型：以A公司為例
★ 風扇壽命預測使用大數據分析－以 X 公司為例	★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-7-29以後開放)

摘要(中)

線上顧客評論在電子商務中扮演著至關重要的角色，假評論的出現對電子商務生態
系統造成了負面影響，因此，開發有效的假評論偵測方法成為了一個重要的研究課題。近
年來，雖然有一些研究使用生成式 AI 來生成假評論並分析其生成機制，然而，這些研究
大多僅著重於潤飾或改寫現有評論，並未對假評論的多種面向進行深入探討。因此，本研
究旨在開發有效的假評論偵測方法，並分析由 ChatGPT 生成的各種假評論面向對偵測表
現的影響。本研究使用 YelpZip 資料集，通過文本預處理、多種特徵提取方法、重新採樣
技術，並結合多種機器學習與深度學習進行實驗，如隨機森林、eXtreme Gradient Boosting
(XGBoost)、邏輯迴歸、Bidirectional Encoder Representations from Transformers (BERT)及
Robustly Optimized BERT Approach (RoBERTa)，以找出最有效的假評論偵測方法。本研究
使用 YelpZip 資料集與 ChatGPT 生成不同種類的假評論，包含單純潤飾後的假評論、含有
不同餐廳資訊的假評論以及含有不同用餐經驗的假評論，深入分析不同面向的評論對假評
論偵測效果的影響。研究結果顯示，在傳統機器學習方法中，使用 BERT 嵌入、隨機過採
樣和邏輯迴歸組合達到最佳表現 (AUC：0.715)。在深度學習方法中，RoBERTa 表現最佳
(AUC：0.770)。根據不同假評論面向的偵測結果，說明 ChatGPT 修飾後的假評論較容易
辨識以及評論中餐廳資訊與用餐經驗的取得程度對於假評論偵測的影響。本研究運用
ChatGPT 潤飾與生成不同面向的假評論，深入分析其對假評論偵測效能的影響，為假評論
偵測研究提供新的視角與思路。

摘要(英)

Online customer reviews play a crucial role in e-commerce, but the emergence of fake
reviews negatively impacts the e-commerce ecosystem. Developing effective methods for fake
reviews detection (FRD) has become an important research domain. Recent studies using
generative AI to create fake reviews focus on embellishing existing reviews. This research aims to
devise efficacious FRD methods and evaluate the effects of different aspects of fake reviews
generated by ChatGPT on detection performance. We use the YelpZip dataset and apply multiple
feature extraction methods, resampling techniques, and combining various machine learning (ML)
and deep learning (DL) approaches, such as Random Forest, eXtreme Gradient Boosting
(XGBoost), Logistic Regression (LR), Bidirectional Encoder Representations from Transformers
(BERT), and Robustly Optimized BERT Approach (RoBERTa), to find the most effective method.
Different types of fake reviews were generated using ChatGPT, including simply embellished fake
reviews, those containing different restaurant information, and reviews with various dining
experiences. We deeply analyze how various review aspects impact FRD performance. The results
show that among ML methods, the combination of BERT embeddings, Random Oversampling
(ROS), and LR achieved the best performance (AUC: 0.715). In DL methods, RoBERTa
performed the best (AUC: 0.770). The detection results suggest that ChatGPT-rephrased fake
reviews are easier to identify, and the inclusion of restaurant information and dining experiences
in the reviews impacts the performance of FRD. This study leverages ChatGPT to rephrase and
create fake reviews with varying aspects, providing new perspectives and valuable insights in fake
reviews detection.

關鍵字(中)

★ 假評論偵測
★ 文字探勘
★ 監督式學習
★ 遷移式學習
★ 生成式 AI

關鍵字(英)

★ Fake review detection
★ Text mining
★ Supervised learning
★ Transfer learning
★ Generative AI

論文目次

摘要 ........................................................................................................................................................ i
Abstract .................................................................................................................................................ii
Acknowledgments .............................................................................................................................. iii
Table of Contents ................................................................................................................................. iv
List of Figures ...................................................................................................................................... vi
List of Table ........................................................................................................................................vii
1. Introduction................................................................................................................................... 1
1.1 Background........................................................................................................................... 1
1.2 Motivation ............................................................................................................................ 2
1.3 Research Objective .............................................................................................................. 4
2. Literature Review ......................................................................................................................... 5
2.1 Feature Utilization in Fake Review Detection ................................................................... 5
2.1.1 Review-based Feature...................................................................................................... 5
2.1.2 Reviewer-based Feature .................................................................................................. 7
2.2 Incorporating Generative AI in Fake Review Detection ................................................... 9
3. Methodology ............................................................................................................................... 12
3.1 YelpZip ............................................................................................................................... 13
3.2 Data Preprocessing ............................................................................................................. 13
3.2.1 Feature Extraction .......................................................................................................... 13
3.2.2 Resample ........................................................................................................................ 18
v
3.3 Model Construction ........................................................................................................... 18
3.4 Review Generation with ChatGPT ................................................................................... 21
3.4.1 Rephrase ......................................................................................................................... 21
3.4.2 Restaurant Information .................................................................................................. 22
3.4.3 Dining Experience ......................................................................................................... 23
3.5 Experiment Evaluation ...................................................................................................... 25
3.5.1 Experiment Procedure ................................................................................................... 25
3.5.2 Implementation details .................................................................................................. 26
3.5.3 Evaluation Metrics ......................................................................................................... 27
4. Experiment Result & Discussion .............................................................................................. 29
4.1 Experiment 1 ...................................................................................................................... 29
4.2 Experiment 2 ...................................................................................................................... 38
5. Conclusions and Future Work ................................................................................................... 44
5.1 Conclusion .......................................................................................................................... 44
5.2 Future Work........................................................................................................................ 45
Reference ............................................................................................................................................. 47

參考文獻

Adelani, D. I., Mai, H., Fang, F., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2020). Generating
Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their
Human- and Machine-Based Detection. In L. Barolli, F. Amato, F. Moscato, T. Enokido,
& M. Takizawa (Eds.), Advanced Information Networking and Applications (Vol. 1151,
pp. 1341–1354). Springer International Publishing. https://doi.org/10.1007/978-3-030-
44041-1_114
Ahmed, S., & Muhammad, F. (2019). Using Boosting Approaches to Detect Spam Reviews. 2019
1st International Conference on Advances in Science, Engineering and Robotics
Technology (ICASERT), 1–6. https://doi.org/10.1109/ICASERT.2019.8934467
Banerjee, S., Chua, A. Y. K., & Kim, J. (2017). Don’t be deceived: Using linguistic analysis to
learn how to discern online review authenticity. Journal of the Association for Information
Science and Technology, 68(6), 1525–1538. https://doi.org/10.1002/asi.23784
Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online
consumer electronics retailers. Information Processing & Management, 56(4), 1234–1244.
https://doi.org/10.1016/j.ipm.2019.03.002
Jabeur, S. B., Ballouk, H., Arfi, W. B., & Sahut, J. M. (2023). Artificial intelligence applications
in fake review detection: Bibliometric analysis and future avenues for research. Journal of
Business Research, 158, 113631. https://doi.org/10.1016/j.jbusres.2022.113631
Birim, Ş. Ö., Kazancoglu, I., Kumar Mangla, S., Kahraman, A., Kumar, S., & Kazancoglu, Y.
(2022). Detecting fake reviews through topic modelling. Journal of Business Research,
149, 884–900. https://doi.org/10.1016/j.jbusres.2022.05.081
48
Carbonell, G., Barbu, C.-M., Vorgerd, L., & Brand, M. (2019). The impact of emotionality and
trust cues on the perceived trustworthiness of online reviews. Cogent Business &
Management.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic
Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16(1),
321–357.
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the
22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,
785–794. https://doi.org/10.1145/2939672.2939785
Dellarocas, C. (2006). Strategic Manipulation of Internet Opinion Forums: Implications for
Consumers and Firms. Management Science, 52(10), 1577–1593.
https://doi.org/10.1287/mnsc.1060.0567
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding (arXiv:1810.04805). arXiv.
http://arxiv.org/abs/1810.04805
Dimoka, A., Hong, Y., & Pavlou, P. A. (2012). On Product Uncertainty in Online Markets: Theory
and Evidence. MIS Quarterly, 36(2), 395–426. https://doi.org/10.2307/41703461
Dong, B., Li, M., & Sivakumar, K. (2019). Online Review Characteristics and Trust: A Cross-
Country Examination. Decision Sciences, 50(3), 537–566.
https://doi.org/10.1111/deci.12339
Duma, R., Niu, Z., Nyamawe, A., Tchaye-Kondi, J., & Yusuf, A. (2023). A Deep Hybrid Model
for fake review detection by jointly leveraging review text, overall ratings, and aspect
ratings. Soft Computing, 27, 1–16. https://doi.org/10.1007/s00500-023-07897-4
49
Elmogy, A. M. (2021). Fake Reviews Detection using Supervised Machine Learning. International
Journal of Advanced Computer Science and Applications, 12(1).
Filieri, R., Alguezaui, S., & McLeay, F. (2015). Why do travelers trust TripAdvisor? Antecedents
of trust towards consumer-generated media and its influence on recommendation adoption
and word of mouth. Tourism Management, 51, 174–185.
https://doi.org/10.1016/j.tourman.2015.05.007
Fogel, D., Fuentes, J. L., López, C., & Soto, L. M. (2018). Association between an ectoparasitic
copepod (Caligus sp.) and the monogenean Udonella cf. Caligorum Johnston 1835 on a
catfish population. Journal of Helminthology, 92(2), 250–253. Cambridge Core.
https://doi.org/10.1017/S0022149X17000153
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and
sales: The role of reviewer identity disclosure in electronic markets. Information Systems
Research, 19(3), 291–313.
Gambetti, A., & Han, Q. (2023). Dissecting AI-Generated Fake Reviews: Detection and Analysis
of GPT-Based Restaurant Reviews on Social Media.
Gan, Q., Ferns, B. H., Yu, Y., & Jin, L. (2017). A Text Mining and Multidimensional Sentiment
Analysis of Online Restaurant Reviews. Journal of Quality Assurance in Hospitality &
Tourism, 18(4), 465–492. https://doi.org/10.1080/1528008X.2016.1250243
Garcia, L. (2019, January 29). Deception on Amazon—An NLP exploration—Part 1. Medium.
https://medium.com/@lievgarcia/deception-on-amazon-c1e30d977cfd
Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product
reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and
Data Engineering, 23(10), 1498–1512.
50
Gupta, P., Gandhi, S., & Chakravarthi, B. R. (2021). Leveraging Transfer learning techniques-
BERT, RoBERTa, ALBERT and DistilBERT for Fake Review Detection. Forum for
Information Retrieval Evaluation, 75–82. https://doi.org/10.1145/3503162.3503169
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach
for imbalanced learning. 2008 IEEE International Joint Conference on Neural Networks
(IEEE World Congress on Computational Intelligence), 1322–1328.
https://doi.org/10.1109/IJCNN.2008.4633969
Hameed, W., Allami, R., & Ali, Y. (2023). Fake Review Detection Using Machine Learning.
Revue d’Intelligence Artificielle, 37. https://doi.org/10.18280/ria.370507
Ho, T. K. (1995). Random decision forests. Proceedings of 3rd International Conference on
Document Analysis and Recognition, 1, 278–282.
https://doi.org/10.1109/ICDAR.1995.598994
Hunt, K. M. (2015). Gaming the system: Fake online reviews v. Consumer law. Computer Law &
Security Review, 31(1), 3–25.
Hussain, N., Turab Mirza, H., Hussain, I., Iqbal, F., & Memon, I. (2020). Spam Review Detection
Using the Linguistic and Spammer Behavioral Methods. IEEE Access, 8, 53801–53816.
https://doi.org/10.1109/ACCESS.2020.2979226
Ibafiez-Lissen, L., Gonzalez-Manzano, L., de Fuentes, J. M., & Goyanes, M. (2024). Use of
transfer learning for affordable in-context fake review generation.
Nithya, K., Krishnamoorthi, M., Easwaramoorthy, S. V., Dhivyaa, C R, Yoo, S., & Cho, J. (2024).
Hybrid approach of deep feature extraction using BERT– OPCNN & FIAC with
customized Bi-LSTM for rumor text classification. Alexandria Engineering Journal, 90,
65–75. https://doi.org/10.1016/j.aej.2024.01.056
51
Kasiselvanathan, M., Dhanasekar, J., & Prasad, J. (2022). Classification and Analysis of Fake
Product Review using Ai. 21(1).
Kumar, A., Gopal, R. D., Shankar, R., & Tan, K. H. (2022). Fraudulent review detection model
focusing on emotional expressions and explicit aspects: Investigating the potential of
feature engineering. Decision Support Systems, 155, 113728.
https://doi.org/10.1016/j.dss.2021.113728
Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents
(arXiv:1405.4053). arXiv. http://arxiv.org/abs/1405.4053
Li, J., Ott, M., Cardie, C., & Hovy, E. (2014). Towards a General Rule for Identifying Deceptive
Opinion Spam. In K. Toutanova & H. Wu (Eds.), Proceedings of the 52nd Annual Meeting
of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1566–
1576). Association for Computational Linguistics. https://doi.org/10.3115/v1/P14-1147
Li, S., Zhong, G., Jin, Y., Wu, X., Zhu, P., & Wang, Z. (2023). A Deceptive Reviews Detection
Method Based on Multidimensional Feature Construction and Ensemble Feature Selection.
IEEE Transactions on Computational Social Systems, 10(1), 153–165.
https://doi.org/10.1109/TCSS.2022.3144013
Li, W., Gao, S., Zhou, H., Huang, Z., Zhang, K., & Li, W. (2019). The Automatic Text
Classification Method Based on BERT and Feature Union. 2019 IEEE 25th International
Conference on Parallel and Distributed Systems (ICPADS), 774–777.
https://doi.org/10.1109/ICPADS47876.2019.00114
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., &
Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach
(arXiv:1907.11692). arXiv. http://arxiv.org/abs/1907.11692
52
Liu, Y., Zhou, W., & Chen, H. (2017). Efficiently Promoting Product Online Outcome: An
Iterative Rating Attack Utilizing Product and Market Property. IEEE Transactions on
Information Forensics and Security, 12(6), 1444–1457.
https://doi.org/10.1109/TIFS.2017.2668992
Lu, J., Zhan, X., Liu, G., Zhan, X., & Deng, X. (2023). BSTC: A Fake Review Detection Model
Based on a Pre-Trained Language Model and Convolutional Neural Network. Electronics,
12(10), 2165. https://doi.org/10.3390/electronics12102165
Luo, J., Luo, J., Nan, G., & Li, D. (2023). Fake review detection system for online E-commerce
platforms: A supervised general mixed probability approach. Decision Support Systems,
114045. https://doi.org/10.1016/j.dss.2023.114045
Mahinderjit Singh, M., Wern Shen, L., & Anbar, M. (2019). Conceptualizing Distrust Model with
Balance Theory and Multi-Faceted Model for Mitigating False Reviews in Location-Based
Services (LBS). Symmetry, 11(9), 1118.
Mc Laughlin, G. H. (1969). SMOG Grading-a New Readability Formula. Journal of Reading,
12(8), 639–646.
Mir, A. Q., Khan, F. Y., & Chishti, M. A. (2023). Online Fake Review Detection Using Supervised
Machine Learning And BERT Model (arXiv:2301.03225). arXiv.
http://arxiv.org/abs/2301.03225
Mridha, M. F., Keya, A. J., Hamid, Md. A., Monowar, M. M., & Rahman, Md. S. (2021). A
Comprehensive Review on Fake News Detection With Deep Learning. IEEE Access, 9,
156151–156170. https://doi.org/10.1109/ACCESS.2021.3129329
Muliono, Y., Gaol, F. L., Soewito, B., & Warnars, H. L. H. S. (2022). Hoax Classification in
Imbalanced Datasets Based on Indonesian News Title using RoBERTa. 2022 3rd
53
International Conference on Artificial Intelligence and Data Sciences (AiDAS), 264–268.
https://doi.org/10.1109/AiDAS56890.2022.9918747
Munzel, A. (2016). Assisting consumers in detecting fake reviews: The role of identity information
disclosure and consensus. Journal of Retailing and Consumer Services, 32, 96–108.
https://doi.org/10.1016/j.jretconser.2016.06.002
Ott, M., Cardie, C., & Hancock, J. (2012). Estimating the prevalence of deception in online review
communities. In Proceedings of the 21st international conference on World Wide Web (pp.
201–210). Association for Computing Machinery.
https://doi.org/10.1145/2187836.2187864
Ott, M., Cardie, C., & Hancock, J. T. (2013). Negative deceptive opinion spam. Proceedings of
the 2013 Conference of the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, 497–501.
Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011). Finding Deceptive Opinion Spam by Any
Stretch of the Imagination.
Paget, S. (2023, February 7). Local Consumer Review Survey 2023: Customer Reviews and
Behavior. BrightLocal. https://www.brightlocal.com/research/local-consumer-review-
survey/
Perez-Castro, A., Martínez-Torres, M. R., & Toral, S. L. (2023). Efficiency of automatic text
generators for online review content generation. Technological Forecasting and Social
Change, 189, 122380. https://doi.org/10.1016/j.techfore.2023.122380
Petrescu, M., O’Leary, K., Goldring, D., & Ben Mrad, S. (2018). Incentivized reviews: Promising
the moon for a few stars. Journal of Retailing and Consumer Services, 41, 288–295.
https://doi.org/10.1016/j.jretconser.2017.04.005
54
Poongodi, M., Vijayakumar, V., Rawal, B., Bhardwaj, V., Agarwal, T., Jain, A., Ramanathan, L.,
& Sriram, V. P. (2019). Recommendation model based on trust relations & user credibility.
Journal of Intelligent & Fuzzy Systems, 36(5), 4057–4064. https://doi.org/10.3233/JIFS-
169966
Prati, R. C., Batista, G. E. A. P. A., & Monard, M. C. (2004). Learning with Class Skews and
Small Disjuncts. In A. L. C. Bazzan & S. Labidi (Eds.), Advances in Artificial Intelligence
– SBIA 2004 (Vol. 3171, pp. 296–306). Springer Berlin Heidelberg.
https://doi.org/10.1007/978-3-540-28645-5_30
Rao, S., Verma, A. K., & Bhatia, T. (2023). Hybrid ensemble framework with self-attention
mechanism for social spam detection on imbalanced data. Expert Systems with Applications,
217, 119594. https://doi.org/10.1016/j.eswa.2023.119594
Rayana, S., & Akoglu, L. (2015). Collective Opinion Spam Detection: Bridging Review Networks
and Metadata. Proceedings of the 21th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, 985–994.
https://doi.org/10.1145/2783258.2783370
Raza, A. (2021). COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR
FAKE REVIEW DETECTION. 13.
Refaeli, D., & Hajek, P. (2021). Detecting Fake Online Reviews using Fine-tuned BERT. 2021
5th International Conference on E-Business and Internet, 76–80.
https://doi.org/10.1145/3497701.3497714
Salminen, J., Kandpal, C., Kamel, A. M., Jung, S., & Jansen, B. J. (2022). Creating and detecting
fake reviews of online products. Journal of Retailing and Consumer Services, 64, 102771.
https://doi.org/10.1016/j.jretconser.2021.102771
55
Budhi, G. S., Chiong, R., Wang, Z., & Dhakal, S. (2021). Using a hybrid content-based and
behaviour-based featuring approach in a parallel environment to detect fake reviews.
Electronic Commerce Research and Applications, 47, 101048.
https://doi.org/10.1016/j.elerap.2021.101048
Saxena, B., Goyal, S., Kumari, A., & Agarwal, A. (2022). Boosting Accuracy of Fake Review
Prediction Using Synthetic Minority Oversampling Technique. 2022 International
Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 156–161.
https://doi.org/10.1109/ICCCIS56430.2022.10037717
Shan, G., Zhou, L., & Zhang, D. (2021). From conflicts and confusion to doubts: Examining
review inconsistency for fake review detection. Decision Support Systems, 144, 113513.
https://doi.org/10.1016/j.dss.2021.113513
Sharma, A. (2020). Impact of Online Reviews on Customer Perception and Buying Behavior.
Supremo Amicus, 21, 657.
Shetty, S. C. (2019). Learning to detect fake online reviews using readability tests and text
analytics.
Singhal, R., & Kashef, R. (2024). A Weighted Stacking Ensemble Model With Sampling for Fake
Reviews Detection. IEEE Transactions on Computational Social Systems, 11(2), 2578–
2594. https://doi.org/10.1109/TCSS.2023.3268548
Sivaramakrishnan, N., & Subramaniyaswamy, V. (2016). Recommendation system with
demographic attributes for fake review identification. Research Journal of Pharmaceutical
Biological and Chemical Sciences, 7(6), 891–899.
56
Smith, K. T. (2011). Digital marketing strategies that Millennials find appealing, motivating, or
just annoying. Journal of Strategic Marketing, 19(6), 489–499.
https://doi.org/10.1080/0965254X.2011.581383
Song, W., Park, S., & Ryu, D. (2017). Information quality of online reviews in the presence of
potentially fake reviews. Korean Economic Review, 33(1), 5–34.
Vidanagama, D. U., Silva, T. P., & Karunananda, A. S. (2020). An Approach to Detect Fake
Reviews based on Logistic Regression using Review-Centric Features. Artificial
Intelligence Review, 53(2), 1323–1352. https://doi.org/10.1007/s10462-019-09697-5
Wang, N., Yang, J., Kong, X., & Gao, Y. (2022). A fake review identification framework
considering the suspicion degree of reviews with time burst characteristics. Expert Systems
with Applications, 190, 116207. https://doi.org/10.1016/j.eswa.2021.116207
Wang, Z., Huang, Z., & Gao, J. (2020). Chinese Text Classification Method Based on BERT Word
Embedding. Proceedings of the 2020 5th International Conference on Mathematics and
Artificial Intelligence, 66–71. https://doi.org/10.1145/3395260.3395273
Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis,
and directions for future research. Decision Support Systems, 132, 113280.
https://doi.org/10.1016/j.dss.2020.113280
Youden, W. J. (1950). Index for rating diagnostic tests. Cancer, 3(1), 32–35.
Zhang, W., Du, Y., Yoshida, T., & Wang, Q. (2018). DRI-RCNN: An approach to deceptive
review identification using recurrent convolutional neural network. Information
Processing & Management, 54(4), 576–592. https://doi.org/10.1016/j.ipm.2018.03.007
Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2013). Modeling consumer learning from online
product reviews. Marketing Science, 32(1), 153–169.
57
Zhou, H. (2022). Research of Text Classification Based on TF-IDF and CNN-LSTM. Journal of
Physics: Conference Series, 2171(1), 012021. https://doi.org/10.1088/1742-
6596/2171/1/012021
Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of
product and consumer characteristics. Journal of Marketing, 74(2), 133–148.
Zhuang, M., Cui, G., & Peng, L. (2018). Manufactured opinions: The effect of manipulating online
product reviews. Journal of Business Research, 87, 24–35.
https://doi.org/10.1016/j.jbusres.2018.02.016

指導教授

胡雅涵

審核日期

2024-7-29

推文