Abstract
With the increase of web use in Morocco today, Internet has become an important source of information. Specifically, across social media, Moroccan people use several languages in their communication leaving behind unstructured user-generated text that present several opportunities for Natural Language Processing. Among languages found in this data, Moroccan Dialectal Arabic stands with an important content and several features. In this paper, we investigate online written text generated by Moroccan users in social media with an emphasis on Moroccan Dialectal Arabic. For this purpose, we follow several steps, using some tools such as a language identification system, in order to conduct a deep study of this data. The most interesting findings that have emerged is the use of code switching, multi-script and low amount of words in Moroccan UGT text.
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Notes
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According to Hootsuite https://hootsuite.com.
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Using Facebook Page Post Scraper https://github.com/minimaxir/facebook-page-post-scraper.
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References
Itani, M.: Sentiment Analysis and Resources for Informal Arabic Text on Social Media. Sheffield Hallam University, Sheffield (2018)
Hu, X., Liu, H.: Text analytics in social media. In: Mining Text Data, pp. 385–414. Springer, US (2012)
Liu, X., Wang, M., Huet, B.: Event analysis in social multimedia: a survey. Front. Comput. Sci. 10(3), 433–446 (2016)
Tachicart, R., Bouzoubaa, K., Jaafar, H.: Lexical differences and similarities between Moroccan dialect and Arabic. In: 4th IEEE International Colloquium on Information Science and Technology (CIST 2016), Tangier (2016)
Sadat, F., Kazemi, F., Farzindar, A.: Automatic identification of Arabic language varieties and dialects in social media. In: Second Workshop on Natural Language Processing for Social Media (SocialNLP 2014), Queensland, Australia (2014)
Voss, C., Tratz, S., Laoudi, J., Briesch, D.: Finding romanized Arabic dialect in code-mixed tweets. In: Ninth International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland (2014)
Albogamy, F., Ramsay, A.: Unsupervised stemmer for Arabic tweets. In: Second Workshop on Noisy User-generated Text (WNUT 2016), Osaka, Japan (2016)
Afli, H., Aransa, W., Lohar, P., Way, A.: From Arabic user-generated content to machine translation: integrating automatic error correction. In: 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2016), Konya, Turkey (2016)
Zaghouani, W., et al.: Large scale arabic error annotation: guidelines and framework. In: Ninth International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland (2014)
Habash, N., Rambow, O., Roth, R.: Mada+tokan: a toolkit for arabic tokenization, diacritization, morphological disambiguation, POS tagging, stemming and lemmatization. In: Second International Conference on Arabic Language Resources and Tools (MEDAR 2009), Cairo, Egypt (2009)
Abidi, K., Smaïli, K.: An empirical study of the Algerian dialect of Social network. In: International Conference on Natural Language, Signal and Speech Processing (ICNLSP 2019), Casablanca, Morocco (2017)
Salloum, S.A., Al-Emran, M., Abdel Monem, A., Shaalan, K.: A survey of text mining in social media: Facebook and Twitter perspectives. Adv. Sci. Technol. Eng. syst. J. 2(1), 127–133 (2017)
Irfan, R., et al.: A survey on text mining in social networks. Knowl. Eng. Rev. 30(2), 157–170 (2015)
Bies, A., et al.: Transliteration of Arabizi into Arabic orthography: developing a parallel annotated Arabizi-Arabic script SMS/Chat corpus. In: First Workshop on Computational Approaches to Code Switching (EMNLP 2014), Doha, Qatar (2014)
Jaafar, Y., Bouzoubaa, K.: Arabic natural language processing from software engineering to complex pipeline. In: First International Conference on Arabic Computational Linguistics (ACLing 2015), vol. 19, no. 2 (2015)
Tachicart, R., Bouzoubaa, K., Aouragh, S.L., Jaafa, H.: Automatic identification of Moroccan colloquial Arabic. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds.) ICALP 2017. CCIS, vol. 782, pp. 201–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73500-9_15
Lui, M., Baldwin, T.: langid.py: an off-the-shelf language identification tool. In: 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju, Republic of Korea (2012)
Acknowledgements
We would like to thank the student annotators and their Professors Jamal Ezzouaine and Hakima Khamar from Mohammed V University in Rabat for their efforts on the annotation of the Moroccan user-generated text, which were necessary to classify this content according to languages.
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Tachicart, R., Bouzoubaa, K. (2019). An Empirical Analysis of Moroccan Dialectal User-Generated Text. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_1
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