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u00a0Aarushi Gupta, K.C. Tripathi, M.L. Sharma, Pulkit Sharma,
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nJanuary 27, 2023 at 5:33 am
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nAbstract
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Nowadays, the news is being generated each second from every corner of the world, with millions of news articles generated every day. Some assume that at least 1.8 million articles are published yearly, in about 28,000 journals. It has become difficult to recognize what’s fake and what’s genuine due to the overflow of millions of articles every day. Not every person reads every news, so the classification of news according to people’s interests has become significant. 80% of the information is unstructured and “text” being the most common type of unstructured data has led to the emergence of numerous news classification methods. Some of the modern news classification methods stretch from using Machine Learning models like “SVM”, “Naive Bayes”, “Decision Tree” to the modern complex transformer and deep learning models like “BERT” and “LSTM”. So, the main aim of this review is to better understand the pipeline and shortcomings of the news classification processes aforementioned. This article will help researchers to distinguish between various models and choose the one best for their projects.
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Keywords news classification, machine learning, deep learning, data preprocessing
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References
n[if 1104 equals=””]n
1. S. Kaur and N.K. Khiva, “Online news classification using Deep Learning Technique,” International Research Journal of Engineering and Technology, vol. 03, no. 10, pp. 558–563, 2016.
2. A. Khadjeh Nassirtoussi, S. Aghabozorgi, T. Ying Wah, and D.C.L. Ngo, “Text mining for market prediction: A systematic review,” Expert Systems with Applications, vol. 41, no. 16, pp. 7653–7670, Nov. 2014, doi: 10.1016/j.eswa.2014.06.009.
3. R.B. Kumar, B.S. Kumar, and C.S.S. Prasad, “Financial News Classification using SVM,” International Journal of Scientific and Research Publications, vol. 2, no. 3, pp. 1–6, 2012.
4. P. Saigal and V. Khanna, “Multi-category news classification using Support Vector Machine based classifiers,” SN Applied Sciences, vol. 2, no. 3, p. 458, Mar. 2020, doi: 10.1007/s42452- 020-2266-6.
5. D.Y. Liliana, A. Hardianto, and M. Ridok, “Indonesian News Classification using Support Vector Machine,” World Academy of Science, Engineering and Technology, vol. 5, no. 9, pp. 767–770, 2011.
6. Yuliani and S. Sahib, “Hoax News Classification using Machine Learning Algorithms,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 3938–3944, Dec. 2019, doi: 10.35940/ijeat.B3753.129219.
7. S.B. Mangal and D.V. Goyal, “Text News Classification System using Naïve Bayes Classifier,” An International Journal of Engineering Sciences, vol. 3, pp. 209–213, 2014.
8. R. Jehad and S.A. Yousif, “Fake News Classification Using Random Forest and Decision Tree (J48),” Al-Nahrain Journal of Science, vol. 23, no. 4, pp. 49–55, Dec. 2020, doi: 10.22401/ANJS.23.4.09.
9. Y. Muliono and F. Tanzil, “A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 3, no. 2, pp. 157–160, May 2018, doi: 10.30591/jpit.v3i2.828.
10. A.M. Somsen, D. Langbroek, H. Borgman, C. Amrit, and T.X. Bui, Rerouting Digital Transformations: Six Cases in the Airline Industry. HICSS, 2019.
11. G. Kaur and K. Bajaj, “News Classification using Neural Networks,” Communications on Applied Electronics, vol. 5, no. 1, pp. 42–45, May 2016, doi: 10.5120/cae2016652224.
12. M. Hughes, I. Li, S. Kotoulas, and T. Suzumura, “Medical Text Classification using Convolutional Neural Networks,” Informatics for Health: Connected Citizen-Led Wellness and Population Health, pp. 246–250, 2017.
13. T.B. Shahi and A.K. Pant, “Nepali news classification using Naïve Bayes, Support Vector Machines and Neural Networks,” in 2018 International Conference on Communication information and Computing Technology (ICCICT), Mumbai, 2018, pp. 1–5. doi: 10.1109/ICCICT.2018.8325883.
14. Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A Neural Probabilistic Language Model,” Journal of Machine Learning Research, vol. 3, pp. 1137–1155, Mar. 2003.
15. S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning Based Text Classification: A Comprehensive Review,” ACM Computing Surveys (CSUR), vol. 1, no. 1, Jan. 2021, [Online]. Available: http://arxiv.org/abs/2004.03705
16. M. Ali Ramdhani, D.S. Maylawati, and T. Mantoro, “Indonesian news classification using convolutional neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19, no. 2, pp. 1000–1009, Aug. 2020, doi: 10.11591/ijeecs.v19.i2.pp1000-1009.
17. F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein, “Fake News Detection on Social Media using Geometric Deep Learning,” arXiv:1902.06673 [cs, stat], Feb. 2019, [Online]. Available: http://arxiv.org/abs/1902.06673 18. A. Yasin, M. J. Awan, M. F. Shehzad, and M. Ashraf, “Fake News Classification Bimodal using Convolutional Neural Network and Long Short-Term Memory,” International Journal on Emerging Technologies, vol. 11, no. 5, pp. 209–212, 2020.
19. K.S. Nugroho, A.Y. Sukmadewa, and N. Yudistira, “Large-Scale News Classification using BERT Language Model: Spark NLP Approach,” in 6th International Conference on Sustainable Information Engineering and Technology 2021, Malang Indonesia, Sep. 2021, pp. 240–246. doi: 10.1145/3479645.3479658.
20. S. González-Carvajal and E.C. Garrido-Merchán, “Comparing BERT against traditional machine learning text classification,” arXiv:2005.13012 [cs, stat], pp. 1–10, Jan. 2021.
21. A. Aggarwal, A. Chauhan, D. Kumar, M. Mittal, and S. Verma, “Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model-eudl,” EAI Endorsed Transactions on Scalable Information Systems, vol. 7, no. 27, pp. 1–12, 2020, doi: 10.4108/eai.13-7-2018.163973.
22. M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G.S. Choi, and B.-W. On, “Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM),” IEEE Access, vol. 8, pp. 156695– 156706, 2020, doi: 10.1109/ACCESS.2020.3019735.
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Journal Menu
Editors Overview
jocta maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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- By [foreach 286]n
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Aarushi Gupta, K.C. Tripathi, M.L. Sharma, Pulkit Sharma
n
[/foreach]
n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Student, Associate Professor, HOD,Department of Information Technology,Maharaja Agrasen Institute of Technology, Department of Information Technology,Maharaja Agrasen Institute of Technology, Department of Information Technology, Maharaja Agrasen Institute of Technology, Department of Information Technology, Maharaja Agrasen Institute of Technology,Delhi, Delhi, Delhi, Delhi,India, India, India, India
n[/if 1175][/foreach]
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Abstract
nNowadays, the news is being generated each second from every corner of the world, with millions of news articles generated every day. Some assume that at least 1.8 million articles are published yearly, in about 28,000 journals. It has become difficult to recognize what’s fake and what’s genuine due to the overflow of millions of articles every day. Not every person reads every news, so the classification of news according to people’s interests has become significant. 80% of the information is unstructured and “text” being the most common type of unstructured data has led to the emergence of numerous news classification methods. Some of the modern news classification methods stretch from using Machine Learning models like “SVM”, “Naive Bayes”, “Decision Tree” to the modern complex transformer and deep learning models like “BERT” and “LSTM”. So, the main aim of this review is to better understand the pipeline and shortcomings of the news classification processes aforementioned. This article will help researchers to distinguish between various models and choose the one best for their projects.n
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Keywords: news classification, machine learning, deep learning, data preprocessing
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications(jocta)]
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Full Text
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Browse Figures
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References
n[if 1104 equals=””]
1. S. Kaur and N.K. Khiva, “Online news classification using Deep Learning Technique,” International Research Journal of Engineering and Technology, vol. 03, no. 10, pp. 558–563, 2016.
2. A. Khadjeh Nassirtoussi, S. Aghabozorgi, T. Ying Wah, and D.C.L. Ngo, “Text mining for market prediction: A systematic review,” Expert Systems with Applications, vol. 41, no. 16, pp. 7653–7670, Nov. 2014, doi: 10.1016/j.eswa.2014.06.009.
3. R.B. Kumar, B.S. Kumar, and C.S.S. Prasad, “Financial News Classification using SVM,” International Journal of Scientific and Research Publications, vol. 2, no. 3, pp. 1–6, 2012.
4. P. Saigal and V. Khanna, “Multi-category news classification using Support Vector Machine based classifiers,” SN Applied Sciences, vol. 2, no. 3, p. 458, Mar. 2020, doi: 10.1007/s42452- 020-2266-6.
5. D.Y. Liliana, A. Hardianto, and M. Ridok, “Indonesian News Classification using Support Vector Machine,” World Academy of Science, Engineering and Technology, vol. 5, no. 9, pp. 767–770, 2011.
6. Yuliani and S. Sahib, “Hoax News Classification using Machine Learning Algorithms,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 3938–3944, Dec. 2019, doi: 10.35940/ijeat.B3753.129219.
7. S.B. Mangal and D.V. Goyal, “Text News Classification System using Naïve Bayes Classifier,” An International Journal of Engineering Sciences, vol. 3, pp. 209–213, 2014.
8. R. Jehad and S.A. Yousif, “Fake News Classification Using Random Forest and Decision Tree (J48),” Al-Nahrain Journal of Science, vol. 23, no. 4, pp. 49–55, Dec. 2020, doi: 10.22401/ANJS.23.4.09.
9. Y. Muliono and F. Tanzil, “A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 3, no. 2, pp. 157–160, May 2018, doi: 10.30591/jpit.v3i2.828.
10. A.M. Somsen, D. Langbroek, H. Borgman, C. Amrit, and T.X. Bui, Rerouting Digital Transformations: Six Cases in the Airline Industry. HICSS, 2019.
11. G. Kaur and K. Bajaj, “News Classification using Neural Networks,” Communications on Applied Electronics, vol. 5, no. 1, pp. 42–45, May 2016, doi: 10.5120/cae2016652224.
12. M. Hughes, I. Li, S. Kotoulas, and T. Suzumura, “Medical Text Classification using Convolutional Neural Networks,” Informatics for Health: Connected Citizen-Led Wellness and Population Health, pp. 246–250, 2017.
13. T.B. Shahi and A.K. Pant, “Nepali news classification using Naïve Bayes, Support Vector Machines and Neural Networks,” in 2018 International Conference on Communication information and Computing Technology (ICCICT), Mumbai, 2018, pp. 1–5. doi: 10.1109/ICCICT.2018.8325883.
14. Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A Neural Probabilistic Language Model,” Journal of Machine Learning Research, vol. 3, pp. 1137–1155, Mar. 2003.
15. S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning Based Text Classification: A Comprehensive Review,” ACM Computing Surveys (CSUR), vol. 1, no. 1, Jan. 2021, [Online]. Available: http://arxiv.org/abs/2004.03705
16. M. Ali Ramdhani, D.S. Maylawati, and T. Mantoro, “Indonesian news classification using convolutional neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 19, no. 2, pp. 1000–1009, Aug. 2020, doi: 10.11591/ijeecs.v19.i2.pp1000-1009.
17. F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein, “Fake News Detection on Social Media using Geometric Deep Learning,” arXiv:1902.06673 [cs, stat], Feb. 2019, [Online]. Available: http://arxiv.org/abs/1902.06673 18. A. Yasin, M. J. Awan, M. F. Shehzad, and M. Ashraf, “Fake News Classification Bimodal using Convolutional Neural Network and Long Short-Term Memory,” International Journal on Emerging Technologies, vol. 11, no. 5, pp. 209–212, 2020.
19. K.S. Nugroho, A.Y. Sukmadewa, and N. Yudistira, “Large-Scale News Classification using BERT Language Model: Spark NLP Approach,” in 6th International Conference on Sustainable Information Engineering and Technology 2021, Malang Indonesia, Sep. 2021, pp. 240–246. doi: 10.1145/3479645.3479658.
20. S. González-Carvajal and E.C. Garrido-Merchán, “Comparing BERT against traditional machine learning text classification,” arXiv:2005.13012 [cs, stat], pp. 1–10, Jan. 2021.
21. A. Aggarwal, A. Chauhan, D. Kumar, M. Mittal, and S. Verma, “Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model-eudl,” EAI Endorsed Transactions on Scalable Information Systems, vol. 7, no. 27, pp. 1–12, 2020, doi: 10.4108/eai.13-7-2018.163973.
22. M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G.S. Choi, and B.-W. On, “Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM),” IEEE Access, vol. 8, pp. 156695– 156706, 2020, doi: 10.1109/ACCESS.2020.3019735.
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Journal of Computer Technology & Applications
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| Volume | 12 |
| Issue | 3 |
| Received | December 13, 2021 |
| Accepted | December 20, 2021 |
| Published | December 27, 2021 |
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