Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

ArWordVec: efficient word embedding models for Arabic tweets

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

One of the major advances in artificial intelligence nowadays is to understand, process and utilize the humans’ natural language. This has been achieved by employing the different natural language processing (NLP) techniques along with the aid of the various deep learning approaches and architectures. Using the distributed word representations to substitute the traditional bag-of-words approach has been utilized very efficiently in the last years for many NLP tasks. In this paper, we present the detailed steps of building a set of efficient word embedding models called ArWordVec that are generated from a huge repository of Arabic tweets. In addition, a new method for measuring Arabic word similarity is introduced that has been used in evaluating the performance of the generated ArWordVec models. The experimental results show that the performance of the ArWordVec models overcomes the recently available models on Arabic Twitter data for the word similarity task. In addition, two of the large Arabic tweets datasets are used to examine the performance of the proposed models in the multi-class sentiment analysis task. The results show that the proposed models are very efficient and help in achieving a classification accuracy ratio exceeding 73.86% with a high average F1 value of 74.15.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Al-Azani S, El-Alfy ESM (2017) Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text. Procedia Comput. Sci. 109:359–366

    Article  Google Scholar 

  • Al-Twairesh N, Al-Khalifa H, Al-Salman A (2016) AraSenTi: large-scale twitter-specific Arabic sentiment lexicons. In: The 54th annual meeting of the association for computational linguistics (ACL)

  • Ananiadou S, Thompson P, Nawaz R (2013) Enhancing search: events and their discourse context. In: International conference on intelligent text processing and computational linguistics. Springer, Berlin, Heidelberg, pp 318–334

  • Almarwani N, Diab M (2017) Arabic textual entailment with word embeddings. In: The 3rd Arabic natural language processing workshop (WANLP), pp 185–190

  • Batista-Navarro RT, Kontonatsios G, Mihăilă C, Thompson P, Rak R, Nawaz R, Korkontzelos I, Ananiadou S (2013) Facilitating the analysis of discourse phenomena in an interoperable NLP platform. In: International conference on intelligent text processing and computational linguistics. Springer, Berlin, Heidelberg, pp 559–571

  • Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155

    MATH  Google Scholar 

  • El-Mawass N, Alaboodi S (2016) Detecting Arabic spammers and content polluters on Twitter. In: 2016 6th international conference on digital information processing and communications, ICDIPC 2016

  • Fahmi A, Abdullah S, Amin F, Ali A (2017) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. J Intell Syst 5:4. https://doi.org/10.1515/jisys-2017-0083

    Article  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali MS (2018a) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput. https://doi.org/10.1007/s00500-018-3242-6

    Article  MATH  Google Scholar 

  • Fahmi A, Amin F, Abdullah S, Ali A (2018b) Cubic fuzzy Einstein aggregation operators and its application to decision making. Int J Syst Sci. https://doi.org/10.1080/00207721.2018.1503356

    Article  MathSciNet  MATH  Google Scholar 

  • Fernandez RC, Mansour E, Qahtan A, Elmagarmid A, Ilyas I, Maden S, Ouzzani M, Stonebraker M, Tand N (2018) Seeping semantics: linking datasets using word embeddings for data discovery. In: 34th IEEE international conference on data engineering

  • Glove-python (2016). https://github.com/maciejkula/glove-python

  • Howells K, Ertugana A (2017) Applying fuzzy logic for sentiment analysis of social media network data in marketing. In: 9th international conference on theory and application of soft computing, computing with words and perception, ICSCCW 2017

  • Indhuja K, Reghu Raj P C (2014) Fuzzy logic based sentiment analysis of product review documents. In: 2014 1st international conference on computational systems and communications (ICCSC)

  • Kumar D, Shaalan Y, Zhang X, Chan J (2018) Identifying singleton spammers via spammer group detection. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics)

  • Jahangir M, Afzal H, Ahmed M, Khurshid K, Nawaz R (2017) An expert system for diabetes prediction using auto tuned multi-layer perceptron. In: 2017 Intelligent systems conference (IntelliSys). IEEE, pp 722–728

  • Lu C, Huang H, Jian P, Wang D, Guo Y-D (2017) A P-LSTM neural network for sentiment classification. In: Kim J, Shim K, Cao L, Lee J-G, Lin X, Moon Y-S (eds) Advances in knowledge discovery and data mining. Springer International Publishing, Cham, pp 524–533

    Chapter  Google Scholar 

  • Luong M-T, Socher R, Manning CD (2013) Better word representations with recursive neural networks for morphology. In: The SIGNLL conference on computational natural language learning (CoNLL-2013)

  • Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space, pp 1–12. https://doi.org/10.1162/153244303322533223

  • Mikolov T, Le QV, Sutskever I (2013b) Exploiting similarities among languages for machine translation. https://doi.org/10.1162/153244303322533223

  • Mohammad SM, Salameh M, Kiritchenko S (2016) How translation alters sentiment. J Artif Intell Res 55:95–130. https://doi.org/10.1613/jair.4787

    Article  MathSciNet  Google Scholar 

  • Nabil M, Aly M, Atiya A (2015) ASTD: Arabic sentiment tweets dataset. In: Proceedings of 2015 conference on empirical methods in natural language processing. https://doi.org/10.18653/v1/D15-1299

  • Nakov P, Ritter A, Rosenthal S, Stoyanov V, Sebastiani F (2016) SemEval-2016 Task 4: sentiment analysis in twitter. In: Proceedings of the 10th international workshop on semantic evaluations (SemEval-2017), pp 1–18

  • Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Rehurek R, Sojka P (2010) Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. https://doi.org/10.13140/2.1.2393.1847

  • Salameh M, Mohammad SM, Kiritchenko S, Canada C (2015) Sentiment after translation: a case-study on Arabic social media posts. In: Human language technologies: the 2015 annual conference of the North American chapter of the ACL, pp 767–777

  • Shardlow M, Batista-Navarro R, Thompson P, Nawaz R, McNaught J, Ananiadou S (2018) Identification of research hypotheses and new knowledge from scientific literature. BMC Med Inform Decis Mak 18(1):46

    Article  Google Scholar 

  • Soliman AB, Eissa K, El-Beltagy SR (2017) AraVec: a set of Arabic word embedding models for use in Arabic NLP. Procedia Comput Sci 117:256–265. https://doi.org/10.1016/j.procs.2017.10.117

    Article  Google Scholar 

  • Wang M, Chen S, He L (2018) Sentiment classification using neural networks with sentiment centroids. In: Phung D, Tseng VS, Webb GI, Ho B, Ganji M, Rashidi L (eds) Advances in knowledge discovery and data mining. Springer International Publishing, Cham, pp 56–67

    Chapter  Google Scholar 

  • Xun G, Li Y, Gao J, Zhang A (2017) Collaboratively improving topic discovery and word embeddings by coordinating global and local contexts. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining—KDD ’17

  • Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv Prepr arXiv:1510.03820. https://doi.org/10.3115/v1/D14-1181

  • Ziani A, Azizi N, Schwab D, Aldwairi M, Chekkai N, Zenakhra D, Cheriguene S (2017) Recommender system through sentiment analysis. In: The 2nd international conference on automatic control, telecommunications and signals

Download references

Acknowledgements

We express our thanks to the administration of the High Performance Computing Center (HPCC) at King Abdulaziz University, Jeddah, Saudi Arabia, for their support and the access to the Aziz Supercomputer that helped us in performing our experiments which require both huge computing capabilities and storage space.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammed M. Fouad.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by Mu-Yen Chen.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fouad, M.M., Mahany, A., Aljohani, N. et al. ArWordVec: efficient word embedding models for Arabic tweets. Soft Comput 24, 8061–8068 (2020). https://doi.org/10.1007/s00500-019-04153-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04153-6

Keywords

Navigation