Skip to main content
SpringerLink
Log in

Enhanced Classification of Sentiment Analysis of Arabic Reviews

  • Conference paper
  • First Online:
Advances in Internet, Data and Web Technologies (EIDWT 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 29))

Abstract

Sentiment analysis is the process of mining textual data in order to extract the author’s opinion, typically expressed as a positive, neutral, or negative attitude towards the written text. It is of great interest and has been extensively studied in the English language. However, sentiment analysis in the Arabic language has not received wide attention and most of the research done on Arabic either focuses on introducing new datasets or new sentiment lexicons. In this paper, we introduce a preprocessing suite that includes morphological processing, emoticon extraction, and negation processing to improve the sentiment analysis. Furthermore, we conduct experiments on sentiment analysis of hotel reviews that target two classification tasks: positive/negative and positive/negative/neutral. Our experimental results using various supervised learning algorithms, including deep learning algorithm, demonstrate the effectiveness of the proposed techniques.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Abdul-Mageed, M., Diab, M.T., Korayem, M.: Subjectivity and sentiment analysis of modern standard Arabi0063. In: ACL–HLT 2011–Proceedings of 49th Annual Meet. Association for Computational Linguistics, vol. 2, pp. 587–591 (2011)

    Google Scholar 

  2. Abdul-Mageed, M., Diab, M.: SANA: a large scale multi-genre, multi-dialect Lexicon for Arabic subjectivity and sentiment analysis. In: Proceedings of the Language Resources and Evaluation Conference, pp. 1162–1169 (2014)

    Google Scholar 

  3. Aly, M., Atiya, A.: LABR: a large scale Arabic book reviews dataset. In: The 51st Annual Meeting of the Association for Computational Linguistics, pp. 494–498 (2013)

    Google Scholar 

  4. Al-Ayyoub, M., Essa, S.B., Alsmadi, I.: Lexicon-based sentiment analysis of Arabic tweets. Int. J. Soc. Netw. Min. 2, 101–114 (2015)

    Article  Google Scholar 

  5. Al-Smadi, M., Qawasmeh, O., Talafha, B., Quwaider, M.: Human annotated Arabic dataset of book reviews for aspect based sentiment analysis. In: Proceedings of 2015 International Conference on Future Internet of Things and Cloud, FiCloud 2015 and 2015 International Conference on Open and Big Data, OBD 2015, pp. 726–730 (2015)

    Google Scholar 

  6. Ariss, O.E., Alnemer, L.M.: Morphology based Arabic sentiment analysis of book reviews. In: Gelbukh, A. (eds.) Computational Linguistics and Intelligent Text Processing, CICLing 2017. Lecture Notes in Computer Science, vol. 10762. Springer, Cham (2018)

    Chapter  Google Scholar 

  7. Biltawi, M., Etaiwi, W., Tedmori, S., Hudaib, A., Awajan, A.: Sentiment classification techniques for Arabic language: a survey. In: 2016 7th International Conference on Information and Communication Systems (ICICS), pp. 339–346 (2016)

    Google Scholar 

  8. Cherif, W., Madani, A., Kissi, M.: Towards an efficient opinion measurement in Arabic comments. Procedia Comput. Sci. 73, 122–129 (2015)

    Article  Google Scholar 

  9. Dahou, A., Xiong, S., Zhou, J., Haddoud, M.H., Duan, P.: Word embeddings and convolutional neural network for Arabic sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 2418–2427 (2016)

    Google Scholar 

  10. Dellarocas, C., (Michael) Zhang, X., Awad, N.F.: Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J. Interact. Mark. 21, 23–45 (2007)

    Article  Google Scholar 

  11. Duan, W., Gu, B., Whinston, A.B.: Do online reviews matter\(?-\)an empirical investigation of panel data. Decis. Support Syst. 45, 1007–1016 (2008)

    Article  Google Scholar 

  12. ElSahar, H., El-Beltagy, S.R.: Building large Arabic multi-domain resources for sentiment analysis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 23–34. Springer, Cham (2015)

    Google Scholar 

  13. Eskander, R., Rambow, O.: SLSA: a sentiment Lexicon for standard Arabic. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2545–2550 (2015)

    Google Scholar 

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

    Article  Google Scholar 

  15. Mohammad, S.M., Salameh, M., Kiritchenko, S.: How translation alters sentiment. J. Artif. Intell. Res. 55, 95–130 (2016)

    Article  MathSciNet  Google Scholar 

  16. Nabil, M., Aly, M., Atiya, A.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2515–2519 (2015)

    Google Scholar 

  17. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)

    Article  Google Scholar 

  18. Pang, B., Lee, L., Rd, H., Jose, S.: Thumbs up? sentiment classification using machine learning techniques. Lang. (Baltim), 79–86 (2002)

    Google Scholar 

  19. Pedregosa, F., Varoquaux, G.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  20. Poria, S., Cambria, E., Winterstein, G., Huang, G.: Bin: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl. Based Syst. 69, 45–63 (2014)

    Article  Google Scholar 

  21. Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108, 42–49 (2016)

    Article  Google Scholar 

  22. Refaee, E., Rieser, V.: An Arabic Twitter corpus for subjectivity and sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, pp. 2268–2273 (2014)

    Google Scholar 

  23. Sarikaya, R., Kirchhoff, K., Schultz, T., Hakkani-Tur, D.: Introduction to the special issue on processing morphologically rich languages. IEEE Trans. Audio Speech Lang. Process. 17, 861–862 (2009)

    Article  Google Scholar 

  24. Al Shboul, B., Al-Ayyouby, M., Jararwehy, Y.: Multi-way sentiment classification of Arabic reviews. In: 2015 6th International Conference on Information and Communication Systems, ICICS 2015, pp. 206–211. IEEE (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Jordan, Amman, Jordan

    Loai Alnemer & Bayan Alammouri

  2. Higher Colleges of Technology, Fujariah, UAE

    Jamal Alsakran

  3. Texas A&M University-Commerce, Commerce, TX, USA

    Omar El Ariss

Authors
  1. Loai Alnemer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bayan Alammouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jamal Alsakran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omar El Ariss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamal Alsakran .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Technical University of Catalonia, Barcelona, Spain

    Fatos Xhafa

  3. Computer Information Science (CIS), Higher Colleges of Technology, Fujairah Campus, Fujairah, United Arab Emirates

    Zahoor Ali Khan

  4. Faculty of Computer Information Science, Higher Colleges of Technology, Dubai Campus, Dubai, United Arab Emirates

    Hamad Odhabi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alnemer, L., Alammouri, B., Alsakran, J., Ariss, O.E. (2019). Enhanced Classification of Sentiment Analysis of Arabic Reviews. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_20

Download citation

Publish with us

Policies and ethics

Search

Navigation