博碩士論文 110423062 詳細資訊



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姓名 林鈺融(Lin Yu-Jung)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 利用集成式過採樣方法解決諷刺偵測之類別不平衡問題
(Handling Class Imbalanced Data in Sarcasm Detection with Ensemble Oversampling Techniques)
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摘要(中) 隨著近年來社交媒體和 Web 2.0 平台的快速發展，越來越多的使用者在網路上分享他們的想法並交換意見。企業理解公眾輿論以改善決策的需求比以往任何時候都更加迫切。然而，傳統的情感分析卻無法準確識別諷刺，其中類別不平衡是一個主要問題，為了解決諷刺偵測中的類別不平衡的問題，本研究提出了六種集成過採樣方法（SEO）來有效發揮不同過採樣演算法的優勢。透過將集成學習的概念應用於過採樣技術，所提出的方法 – random、center、uncentered、cluster random、cluster center和cluster uncentered - 為新生成的諷刺資料提供了不同的選擇方法。在本研究中，採用了SMOTE、ADASYN、polynom-fit-SMOTE、ProWSyn和SMOTE-IPF作為實驗中使用的過採樣演算法，並且使用從Twitter和Reddit收集的兩個類別不平衡的諷刺偵測資料集（即iSarcasmEval和SARC-reduced），將文本經過Word2Vec、GloVe、FastText萃取特徵後進行過採樣與集成，以五個分類器 - 支持向量機、決策樹、隨機森林、極限梯度提升和邏輯斯回歸的分類結果對SEO的性能進行評估。實驗結果顯示，SEO在iSarcasmEval的AUC指標上比起單一過採樣演算法高出了7%，在F1-score上則高出了2%。而SARC-reduced，SEO比起單一演算法在AUC指標有著1.5%的提升，在F1-score則有著1% 的提升。
摘要(英) With the fast growing of social media and web 2.0 platform in recent years, people increasingly share their thoughts and exchange their opinions on the internet. The need for enterprise to understand the public opinion to improve their decision making is greater than ever. However, conventional sentiment analysis fails to accurately identify sarcasm, and class imbalance poses a major challenge in sarcasm detection. In order to handle the class imbalance problem in sarcasm detection, this study proposes six ensemble oversampling methods (SEO) that effectively exploit the advantages of various oversampling algorithms. By applying the concept of ensemble learning to oversampling techniques, the proposed methods - random, center, uncentered, cluster random, cluster center, and cluster uncentered - offer distinct selection approaches for the newly produced sarcastic data. In this study, SMOTE, ADASYN, polynom-fit-SMOTE, ProWSyn, SMOTE_IPF are adopted for the oversampling algorithms in the experiment. Furthermore, two imbalanced sarcasm detection datasets, iSarcasmEval and SARC-reduced, collected from Twitter and Reddit, are utilized. After extracting features from the text using Word2Vec, GloVe, and FastText, oversampling and ensemble techniques are applied. The performance of SEO is evaluated using five classifiers - Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Logistic Regression - based on the classification results. The results shows that the proposed method outperform single oversampling algorithm method by 7% for AUC metric and 2% for F1-score for iSarcasmEval. While the improvement is 1.5% for AUC metric and 1% for F1-score for SARC-reduced.
關鍵字(中) ★ 諷刺偵測
★ 類別不平衡
★ 過採樣
★ 集成式學習		 關鍵字(英) ★ Sarcasm detection
★ Class imbalance
★ Oversampling
★ Ensemble learning
論文目次 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Objective 3
1.4 Organization 3
2 Literature Review 4
2.1 Class Imbalance 4
2.1.1 Data level 4
2.1.2 Algorithm level 5
2.2 Sarcasm Detection 6
2.3 Ensemble Learning 9
3 Methodology 12
3.1 Data Preprocessing 12
3.2 Feature Extraction 13
3.2.1 Word2Vec 13
3.2.2 FastText 14
3.2.3 GloVe 15
3.3 Oversampling Algorithms 15
3.3.1 SMOTE 16
3.3.2 ADASYN 16
3.3.3 Polynom-fit-SMOTE 16
3.3.4 ProWSyn 17
3.3.5 SMOTE-IPF 17
3.4 Ensemble Methods 18
3.5 Classification Techniques 22
4 Experimental Setup and Evaluation 24
4.1 Datasets 24
4.2 Experimental Process 25
4.3 Experimental Setup 26
4.4 Evaluation Metrics 26
5 Experimental Results 28
5.1 iSarcasmEval 28
5.1.1 Experiment 1 Single Oversampling Algorithm 28
5.1.2 Experiment 2 Single Oversampling Algorithm vs. SEO 32
5.2 SARC-reduced 33
5.2.1 Experiment 1 Single Oversampling Algorithm 33
5.2.2 Experiment 2 Single Oversampling Algorithm vs. SEO 37
5.3 Discussion 38
5.3.1 iSarcasmEval 38
5.3.2 SARC-reduced 41
5.3.3 Overall Discussion 45
6 Conclusion 47
6.1 Experimental Findings and Contribution 47
6.2 Limitations and Future Directions 48
References 50
參考文獻 1. Abercrombie, G., & Hovy, D. (2016, August). Putting sarcasm detection into context: The effects of class imbalance and manual labelling on supervised machine classification of twitter conversations. In Proceedings of the ACL 2016 student research workshop (pp. 107-113).
2. Akula, R., & Garibay, I. (2021). Interpretable multi-head self-attention architecture for sarcasm detection in social media. Entropy, 23(4), 394.
3. Amir, S., Wallace, B. C., Lyu, H., & Silva, P. C. M. J. (2016). Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976.
4. Banerjee, A., Bhattacharjee, M., Ghosh, K., & Chatterjee, S. (2020). Synthetic minority oversampling in addressing imbalanced sarcasm detection in social media. Multimedia Tools and Applications, 79(47-48), 35995-36031.
5. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 36, 105-139.
6. Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the association for computational linguistics, 5, 135-146.
7. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
8. Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140.
9. Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural networks, 106, 249-259.
10. Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings 13 (pp. 475-482). Springer Berlin Heidelberg.
11. Chawla, N. V. (2010). Data mining for imbalanced datasets: An overview. Data mining and knowledge discovery handbook, 875-886.
12. 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, 321-357.
13. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
14. Choudhary, R., & Shukla, S. (2021). A clustering based ensemble of weighted kernelized extreme learning machine for class imbalance learning. Expert Systems with Applications, 164, 114041.
15. Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241-258.
16. Du, H., Zhang, Y., Gang, K., Zhang, L., & Chen, Y. C. (2021). Online ensemble learning algorithm for imbalanced data stream. Applied Soft Computing, 107, 107378.
17. Farha, I. A., Oprea, S. V., Wilson, S., & Magdy, W. (2022, July). Semeval-2022 task 6: isarcasmeval, intended sarcasm detection in english and arabic. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (pp. 802-814).
18. Feng, W., Huang, W., & Ren, J. (2018). Class imbalance ensemble learning based on the margin theory. Applied Sciences, 8(5), 815.
19. Fernández, A., García, S., Galar, M., Prati, R. C., Krawczyk, B., Herrera, F., ... & Herrera, F. (2018). Cost-sensitive learning. Learning from Imbalanced Data Sets, 63-78.
20. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2011). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
21. Ghosh, A., & Veale, T. (2016, June). Fracking sarcasm using neural network. In Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 161-169).
22. González, J. Á., Hurtado, L. F., & Pla, F. (2020). Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter. Information Processing & Management, 57(4), 102262.
23. González-Ibánez, R., Muresan, S., & Wacholder, N. (2011, June). Identifying sarcasm in twitter: a closer look. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies (pp. 581-586).
24. Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In Advances in Intelligent Computing: International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I 1 (pp. 878-887). Springer Berlin Heidelberg.
25. Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R., & Mihalcea, R. (2018). CASCADE: Contextual Sarcasm Detection in Online Discussion Forums. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1837–1848).
26. He, H., Bai, Y., Garcia, E. A., & Li, S. (2008, June). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence) (pp. 1322-1328). IEEE.
27. Hercig, T., & Lenc, L. (2017, September). The Impact of Figurative Language on Sentiment Analysis. In RANLP (pp. 301-308).
28. Huda, S., Liu, K., Abdelrazek, M., Ibrahim, A., Alyahya, S., Al-Dossari, H., & Ahmad, S. (2018). An ensemble oversampling model for class imbalance problem in software defect prediction. IEEE access, 6, 24184-24195.
29. Jain, D., Kumar, A., & Garg, G. (2020). Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN. Applied Soft Computing, 91, 106198.
30. Joshi, A., Bhattacharyya, P., & Carman, M. J. (2017). Automatic sarcasm detection: A survey. ACM Computing Surveys (CSUR), 50(5), 1-22.
31. Kamal, A., & Abulaish, M. (2022). Cat-bigru: Convolution and attention with bi-directional gated recurrent unit for self-deprecating sarcasm detection. Cognitive computation, 1-19.
32. Khodak, M., Saunshi, N., & Vodrahalli, K. (2017). A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579.
33. Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., Luo, S., ... & Reyes, M. C. (2021). A comparative performance analysis of data resampling methods on imbalance medical data. IEEE Access, 9, 109960-109975.
34. Kovács, G. (2019). An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets. Applied Soft Computing, 83, 105662.
35. Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221-232.
36. Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1), 1-30.
37. Li, Z., Fan, Y., Jiang, B., Lei, T., & Liu, W. (2019). A survey on sentiment analysis and opinion mining for social multimedia. Multimedia Tools and Applications, 78, 6939-6967.
38. Lim, P., Goh, C. K., & Tan, K. C. (2016). Evolutionary cluster-based synthetic oversampling ensemble (eco-ensemble) for imbalance learning. IEEE transactions on cybernetics, 47(9), 2850-2861.
39. Liu, P., Chen, W., Ou, G., Wang, T., Yang, D., & Lei, K. (2014). Sarcasm detection in social media based on imbalanced classification. In Web-Age Information Management: 15th International Conference, WAIM 2014, Macau, China, June 16-18, 2014. Proceedings 15 (pp. 459-471). Springer International Publishing.
40. Maynard, D. G., & Greenwood, M. A. (2014, March). Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In Lrec 2014 proceedings. ELRA.
41. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
42. Mosavi, A., Sajedi Hosseini, F., Choubin, B., Goodarzi, M., Dineva, A. A., & Rafiei Sardooi, E. (2021). Ensemble boosting and bagging based machine learning models for groundwater potential prediction. Water Resources Management, 35, 23-37.
43. Nikulin, V., McLachlan, G. J., & Ng, S. K. (2009). Ensemble approach for the classification of imbalanced data. In AI 2009: Advances in Artificial Intelligence: 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings 22 (pp. 291-300). Springer Berlin Heidelberg.
44. Noble, W. S. (2006). What is a support vector machine?. Nature biotechnology, 24(12), 1565-1567.
45. Oza, N. C., & Russell, S. J. (2001, January). Online bagging and boosting. In International Workshop on Artificial Intelligence and Statistics (pp. 229-236). PMLR.
46. Palit, I., & Reddy, C. K. (2011). Scalable and parallel boosting with mapreduce. IEEE Transactions on Knowledge and Data Engineering, 24(10), 1904-1916.
47. Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
48. Poria, S., Cambria, E., Hazarika, D., & Vij, P. (2016). A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815.
49. Ptáček, T., Habernal, I., & Hong, J. (2014, August). Sarcasm detection on czech and english twitter. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical papers (pp. 213-223).
50. Rajadesingan, A., Zafarani, R., & Liu, H. (2015, February). Sarcasm detection on twitter: A behavioral modeling approach. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 97-106).
51. Ribeiro, M. H. D. M., & dos Santos Coelho, L. (2020). Ensemble approach based on bagging, boosting and stacking for short-term prediction in agribusiness time series. Applied soft computing, 86, 105837.
52. Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., & Huang, R. (2013, October). Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 conference on empirical methods in natural language processing (pp. 704-714).
53. Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249.
54. Thabtah, F., Hammoud, S., Kamalov, F., & Gonsalves, A. (2020). Data imbalance in classification: Experimental evaluation. Information Sciences, 513, 429-441.
55. Tsur, O., Davidov, D., & Rappoport, A. (2010, May). ICWSM—a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 4, No. 1, pp. 162-169).
56. Vyas, V., & Uma, V. (2019). Approaches to sentiment analysis on product reviews. In Sentiment Analysis and Knowledge Discovery in Contemporary Business (pp. 15-30). IGI global.
57. Wang, G., Sun, J., Ma, J., Xu, K., & Gu, J. (2014). Sentiment classification: The contribution of ensemble learning. Decision support systems, 57, 77-93.
58. Zhang, M., Zhang, Y., & Fu, G. (2016, December). Tweet sarcasm detection using deep neural network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: technical papers (pp. 2449-2460).
指導教授 胡雅涵 周惠文 審核日期 2023-7-24
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