Skip to main content
SpringerLink
Log in

A Comparative Study of Feature Selection Methods for Informal Arabic

  • Conference paper
  • First Online:
Innovation in Information Systems and Technologies to Support Learning Research (EMENA-ISTL 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 7))

Abstract

The advent of web 2.0 and new Big Data technologies has created a diversity of data and information that can be used in many fields of application. The case of opinion mining is of increasing interest to researchers because of its impact on policy, marketing, etc. Through this document, we are interested in the study of sentiments more specifically in informal Arabic. We present a new approach of processing and analysis that is improved through feature selection methods. The experiments we have carried out are based on the comparison of 3 feature selection methods combined with several machine learning algorithms applied on a twitter dataset. Our paper reports the enhanced results (Accuracy of 98%) and shows the importance of feature selection for Arabic Sentiment Analysis.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover 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

Notes

  1. 1.

    https://rapidminer.com/.

  2. 2.

    https://thuijskens.github.io/2017/10/07/feature-selection/.

  3. 3.

    https://www.nltk.org/.

  4. 4.

    https://countwordsfree.com/stopwords/arabic.

  5. 5.

    https://www.nltk.org/_modules/nltk/stem/isri.html.

References

  1. Arab Social Media Report: Social Media and the Internet of Things-Towards Data-Driven Policymaking in the Arab World: Potential, Limits and Concerns, 7th edn, p. 88 (2017)

    Google Scholar 

  2. Korayem, M., Crandall, D., Abdul-mageed, M.: Advanced Machine Learning Technologies and Applications, May 2014, vol. 322, pp. 0–10 (2012)

    Google Scholar 

  3. Duwairi, R.M., Qarqaz, I.: Arabic sentiment analysis using supervised classification. In: Proceedings - 2014 International Conference Future Internet Things Cloud, FiCloud 2014, no. August, pp. 579–583 (2014)

    Google Scholar 

  4. Mustafa, H.H., Mohamed, A., Elzanfaly, D.S.: An enhanced approach for arabic sentiment analysis. Int. J. Artif. Intell. Appl. 8(5), 01–14 (2017)

    Google Scholar 

  5. Ahmad, M., Ahmad, M., Aftab, S., Ali, I., Hameed, N.: Hybrid tools and techniques for sentiment analysis : a review, no. July, pp. 28–33 (2017)

    Google Scholar 

  6. Mäntylä, M.V., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27, 16–32 (2018)

    Article  Google Scholar 

  7. Assiri, A., Emam, A., Aldossari, H.: Arabic sentiment analysis: a survey. Int. J. Adv. Comput. Sci. Appl. 6(12), 75–85 (2016)

    Google Scholar 

  8. Nabil, M., Aly, M., Atiya, A.: ASTD: arabic sentiment tweets dataset, no. September, pp. 2515–2519 (2015)

    Google Scholar 

  9. Altowayan, A.A., Tao, L.: Word embeddings for Arabic sentiment analysis. In: Proceedings - 2016 IEEE International Conference Big Data, Big Data 2016, pp. 3820–3825 (2016)

    Google Scholar 

  10. Abdulla, N.A., Ahmed, N.A., Shehab, M.A., Al-ayyoub, M.: Arabic sentiment analysis. In: Jordan Conference on Applied Electrical Engineering Computing Technologies, vol. 6, no. 12, pp. 1–6 (2013)

    Google Scholar 

  11. Alayba, A.M., Palade, V., England, M., Iqbal, R.: Improving sentiment analysis in arabic using word representation. In: 2nd IEEE International Workshop on Arabic Derivated Script Analysis Recognition, ASAR 2018, pp. 13–18 (2018)

    Google Scholar 

  12. Heikal, M., Torki, M., El-Makky, N.: Sentiment Analysis of Arabic Tweets using Deep Learning. Procedia Comput. Sci. 142, 114–122 (2018)

    Article  Google Scholar 

  13. Duwairi, R.M., Marji, R., Sha’Ban, N., Rushaidat, S.: Sentiment analysis in arabic tweets. In: 2014 5th International Conference Information Communication System ICICS 2014, no. April (2014)

    Google Scholar 

  14. Al-harbi, O.: A comparative study of feature selection methods for dialectal arabic sentiment classification using support vector machine, vol. 19, no. 1, pp. 167–176 (2019)

    Google Scholar 

  15. Agarwal, B., Mittal, N.: LNCS 7817 - Optimal Feature Selection for Sentiment Analysis, pp. 13–24 (2013)

    Chapter  Google Scholar 

  16. Savoy, O.: Feature Selection in Sentiment Analysis. Proc. CORIA, no. January, pp. 273–284 (2012)

    Google Scholar 

  17. Chang, G., Huo, H.: A method of fine-grained short text sentiment analysis based on machine learning. Neural Netw. World 28(4), 325–344 (2018)

    Article  Google Scholar 

  18. Asim, M.N., Wasim, M., Ali, M.S., Rehman, A.: Comparison of feature selection methods in text classification on highly skewed datasets. In: 2017 1st International Conference Latest Trends Electrical Engineering and Computing Technologies INTELLECT 2017, vol. 2018-Janua, no. May 2018, pp. 1–8 (2018)

    Google Scholar 

  19. Yang, Y., Pedersen, J.O.: Comparative study on feature selection methods in text categorization, 2554

    Google Scholar 

  20. Kim, H.: Rde-39–74, vol. 7658, pp. 74–77 (2014)

    Google Scholar 

  21. Zerrouki‏, T.: Pyarabic, An Arabic language library for Python (2010). https://pypi.python.org/pypi/pyarabic/

Download references

Author information

Authors and Affiliations

  1. Faculty of Science and Technology, IR2M Laboratory, Hassan 1st University, Settat, Morocco

    Soukaina Mihi, Brahim Ait Ben Ali, Ismail El Bazi, Sara Arezki & Nabil Laachfoubi

Authors
  1. Soukaina Mihi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Brahim Ait Ben Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ismail El Bazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sara Arezki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nabil Laachfoubi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soukaina Mihi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco

    Mohammed Serrhini

  2. Department of Computer Science, School of Management Sciences, Engineering and Technology, Atlantica University, Lisbon, Portugal

    Carla Silva

  3. Department of Computer Science, Taif University, College of Computers and Information Technology, Al Huwaya, Saudi Arabia

    Sultan Aljahdali

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

Mihi, S., Ali, B.A.B., Bazi, I.E., Arezki, S., Laachfoubi, N. (2020). A Comparative Study of Feature Selection Methods for Informal Arabic. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_22

Download citation

Publish with us

Policies and ethics

Search

Navigation