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Journals / Sakarya University Journal of Computer and Information Sciences / 2022 / Cilt: 5 Sayı: 1

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

Ekin EKİNCİ
Pages
121–133
Publication Date
2022-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Our experiment results on imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The classification of documents is one of the problems studied since ancient times and still continues to be studied.
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Key Findings & Data:With the social media becoming a part of daily life and its misuse, the importance of text classification has started to increase.
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Conclusion & Impact:Our experiment results on imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:sentence generation · imbalance classification · offensive language

Abstract

The classification of documents is one of the problems studied since ancient times and still continues to be studied. With the social media becoming a part of daily life and its misuse, the importance of text classification has started to increase. This paper investigates the effect of data augmentation with sentence generation on classification performance in an imbalanced dataset. We propose an LSTM based sentence generation method, Term Frequency-Inverse Document Frequency (TF-IDF) and Word2vec and apply Logistic Regression (LR), Support Vector Machine (SVM), K Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Extremly Randomized Trees (Extra tree), Random Forest, eXtreme Gradient Boosting (Xgboost), Adaptive Boosting (AdaBoost) and Bagging. Our experiment results on imbalanced Offensive Language Identification Dataset (OLID) that machine learning with sentence generation significantly outperforms.

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Cite This Article

EKİNCİ, E. (2022). Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM. Sakarya University Journal of Computer and Information Sciences, Cilt: 5 Sayı: 1, 121–133. https://ajindex.com/articles/946647

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References (14)

  1. S. Rosenthal, P. Atanasova, G. Karadzhov, M. Zampieri, and P. Nakov, "OLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification," arXiv preprint arXiv:2004.14454, 2020
  2. G. Wiedemann, E. Ruppert, R. Jindal and C. Biemann, "Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter," arXiv preprint arXiv:1811.02906v1, 2018
  3. H. Mubarak and K. Darwish K., "Arabic Offensive Language Classification on Twitter," Lecture Notes in Computer Science. Springer, Cham, 2019
  4. E. Ekinci, S. İlhan Omurca and S. Sevim, "Improve Offensive Language Detection with Ensemble Classifiers," IJISAE, vol. 8, no. 2, pp. 109–115, 2020
  5. M. Djandji, F. Baly, W. Antoun and H. Hajj, "Multi-Task Learning using AraBert for Offensive Language Detection," Proc. - 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pp. 97–101, 2020
  6. Y. Tung and Y. Q. Zhang, "Granular SVM with Repetitive Undersampling for Highly Imbalanced Protein Homology Prediction," Proc. - 2006 IEEE International Conference on Granular Computing, pp. 457–460, 2006
  7. J. Brownlee, Imbalanced Classification with Python. Machine Learning Mastery, 2020
  8. Q. Zou, S. Xie, Z. Lin, M. Wu and Y. Ju, "Imbalanced classification is one of most popular topics in the field of machine learning," Big Data Res., vol. 5, pp. 2–8, 2016
  9. L. Wang, H. Cheng, Z. Zheng, A. Yang and X. Zhu, " Ponzi scheme detection via oversampling-based Long Short-Term Memory for smart contracts," Knowl Based Syst., vol. 228, pp.1–12, 2021
  10. A. Gosain and S. Sardana, "Handling Class Imbalance Problem using Oversampling Techniques: A Review," Proc. - 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 79–85, 2017
  11. E. L. Iglesias, A. S. Vieira and L. Borrajo, " An HMM-based over-sampling technique to improve text classification," Expert Syst. Appl., 465, pp. 1–20, 2013
  12. N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," J Artif Intell Res., vol. 16, pp. 321–357, 2002
  13. H. A. Majzoub, I. Elgedawy, Ö. Akaydın and M. Köse Ulukök, " HCAB SMOTE: A Hybrid Clustered Afnitive Borderline SMOTE Approach for Imbalanced Data Binary Classifcation," Arab. J. Sci. Eng., vol. 45, pp. 3205–3222, 2020
  14. G. Douzas, F. Bacao and F. Last, "Improving imbalanced learning through a heuristic over-sampling method bas