Skip to main content
SpringerLink
Log in

Political Articles Categorization Based on Different Naïve Bayes Models

  • Conference paper
  • First Online:
Applied Computing to Support Industry: Innovation and Technology (ACRIT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1174))

Abstract

Sentiment analysis plays an important role in most of human activities and has a significant impact on our behaviours. With the development and use of web technology, there is a huge amount of data that represents users opinions in many areas such as politics and business. This paper applied Naïve Bayes (NB) to analyse the opinions by exploring categories from a text and classified it to the right class (Reform, Conservative and Revolutionary). It investigates the effect of using two feature extraction i.e. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods with Naïve Bayes classifiers (Gaussian, Multinomial, Complement and Bernoulli) on the accuracy of classifying Arabic articles. Precision, recall, F1-score and number of correct predict have been used to evaluate the performance of the applied classifiers. The results reveal that, using TF with TF-IDF improved the accuracy to 96.77%. The Complement was deemed the most suitable for our model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)

    Article  Google Scholar 

  2. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  3. Quan, C., Ren, F.: Unsupervised product feature extraction for feature-oriented opinion determination. Inf. Sci. 272, 16–28 (2014)

    Article  Google Scholar 

  4. Smailović, J., Grčar, M., Lavrač, N., Žnidaršič, M.: Stream-based active learning for sentiment analysis in the financial domain. Inf. Sci. 285, 181–203 (2014)

    Article  Google Scholar 

  5. Catal, C., Sevim, U., Diri, B.: Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm. Expert Syst. Appl. 38(3), 2347–2353 (2011)

    Article  Google Scholar 

  6. Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: Bilingual experiments with an Arabic-English corpus for opinion mining. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, pp. 740–745 (2011)

    Google Scholar 

  7. Abdul-Mageed, M., Diab, M.T.: AWATIF: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In: LREC, vol. 515, pp. 3907–3914. Citeseer (2012)

    Google Scholar 

  8. Abdulla, N., Mohammed, S., Al-Ayyoub, M., Al-Kabi, M.: Automatic lexicon construction for Arabic sentiment analysis. In: 2014 International Conference on Future Internet of Things and Cloud, pp. 547–552. IEEE (2014)

    Google Scholar 

  9. Duwairi, R., El-Orfali, M.: A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. J. Inf. Sci. 40(4), 501–513 (2014)

    Article  Google Scholar 

  10. Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: OCA: opinion corpus for Arabic. J. Am. Soc. Inf. Sci. Technol. 62(10), 2045–2054 (2011)

    Article  Google Scholar 

  11. Abdulla, N.A., Al-Ayyoub, M., Al-Kabi, M.N.: An extended analytical study of arabic sentiments. Int. J. Big Data Intell. 1(1–2), 103–113 (2014)

    Article  Google Scholar 

  12. Aly, M., Atiya, A.: Labr: a large scale Arabic book reviews dataset. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 494–498 (2013)

    Google Scholar 

  13. Abdulla, N.A., Ahmed, N.A., Shehab, M.A., Al-Ayyoub, M.: Arabic sentiment analysis: Lexicon-based and corpus-based. In: 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pp. 1–6. IEEE (2013)

    Google Scholar 

  14. Zhang, D.: Bayesian classification. Fundamentals of Image Data Mining. TCS, pp. 161–178. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17989-2_7

    Chapter  Google Scholar 

  15. Raschka, S.: Naive Bayes and text classification i-introduction and theory, arXiv preprint arXiv:1410.5329 (2014)

  16. Geetha, S., Maniyosai, R.: An improved Naive Bayes classifier on imbalanced attributes. Int. J. Organ. Collect. Intell. (IJOCI) 9(2), 1–15 (2019)

    Article  Google Scholar 

  17. Xu, S.: Bayesian Naïve Bayes classifiers to text classification. J. Inf. Sci. 44(1), 48–59 (2018)

    Article  Google Scholar 

  18. McCallum, A., Nigam, K.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, no. 1, pp. 41–48. Citeseer (1998)

    Google Scholar 

  19. Sharma, N., Singh, M.: Modifying Naive Bayes classifier for multinomial text classification. In: 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1–7. IEEE (2016)

    Google Scholar 

  20. Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of Naive Bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning (ICML 2003), pp. 616–623 (2003)

    Google Scholar 

  21. Anagaw, A., Chang, Y.-L.: A new complement naïve Bayesian approach for biomedical data classification. J. Ambient Intell. Hum. Comput. 10, 3889–3897 (2018)

    Article  Google Scholar 

  22. Chen, J., Huang, H., Tian, S., Qu, Y.: Feature selection for text classification with Naïve Bayes. Expert Syst. Appl. 36(3), 5432–5435 (2009)

    Article  Google Scholar 

  23. Tang, B., Kay, S., He, H.: Toward optimal feature selection in naive Bayes for text categorization. IEEE Trans. Knowl. Data Eng. 28(9), 2508–2521 (2016)

    Article  Google Scholar 

  24. Shimodaira, H.: Text classification using naive Bayes. Learn. Data Note 7, 1–9 (2014)

    Google Scholar 

  25. Abd, D.H., Alwan, J.K., Ibrahim, M., Naeem, M.B.: The utilisation of machine learning approaches for medical data classification and personal care system management for sickle cell disease. In: Annual Conference on New Trends in Information & Communications Technology (2017)

    Google Scholar 

  26. Khalaf, M., et al.: Recurrent neural network architectures for analysing biomedical data sets. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 232–237. IEEE (2017)

    Google Scholar 

  27. Oussous, A., Lahcen, A.A., Belfkih, S.: Impact of text pre-processing and ensemble learning on Arabic sentiment analysis. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security, p. 65. ACM (2019)

    Google Scholar 

  28. Mustafa, M., Eldeen, A.S., Bani-Ahmad, S., Elfaki, A.O.: A comparative survey on Arabic stemming: approaches and challenges. Intell. Inf. Manag. 9(02), 39 (2017)

    Google Scholar 

  29. Abooraig, R., Al-Zu’bi, S., Kanan, T., Hawashin, B., Al Ayoub, M., Hmeidi, I.: Automatic categorization of Arabic articles based on their political orientation. Digit. Invest. 25, 24–41 (2018)

    Article  Google Scholar 

  30. Taghva, K., Elkhoury, R., Coombs, J.: Arabic stemming without a root dictionary. In: International Conference on Information Technology: Coding and Computing (ITCC 2005)-Volume II, vol. 1, pp. 152–157. IEEE (2005)

    Google Scholar 

  31. Abd, D.H., Sadiq, A.T., Abbas, A.R.: A New framework for Automatic Extraction Polarity and Target of Articles (2019)

    Google Scholar 

  32. Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Aggarwal, C., Zhai C. (eds.) Mining Text Data, pp. 163–222. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4614-3223-4_6

    Chapter  Google Scholar 

  33. Alowaidi, S., Saleh, M., Abulnaja, O.: Semantic sentiment analysis of Arabic texts. Int. J. Adv. Comput. Sci. Appl. 8(2), 256–262 (2017)

    Google Scholar 

  34. Khalaf, M., et al.: An application of using support vector machine based on classification technique for predicting medical data sets. In: Huang, D.S., Jo, K.H., Huang, Z.K. (eds.) ICIC 2019. LNCS, vol. 11644, pp. 580–591. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-26969-2_55

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Technology, Baghdad, Iraq

    Dhafar Hamed Abd, Ahmed T. Sadiq & Ayad R. Abbas

  2. Department of Computer Science, Al-Maarif University College, Alanbar, Iraq

    Dhafar Hamed Abd

Authors
  1. Dhafar Hamed Abd
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed T. Sadiq
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ayad R. Abbas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dhafar Hamed Abd , Ahmed T. Sadiq or Ayad R. Abbas .

Editor information

Editors and Affiliations

  1. Al Maaref University College, Ramadi, Iraq

    Mohammed I. Khalaf

  2. Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

  3. University of Liverpool, Liverpool, UK

    Alexei Lisitsa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Abd, D.H., Sadiq, A.T., Abbas, A.R. (2020). Political Articles Categorization Based on Different Naïve Bayes Models. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-38752-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38751-8

  • Online ISBN: 978-3-030-38752-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation