Skip to main content

2018 | OriginalPaper | Buchkapitel

Standard and Dialectal Arabic Text Classification for Sentiment Analysis

verfasst von : Mohcine Maghfour, Abdeljalil Elouardighi

Erschienen in: Model and Data Engineering

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In social networks, the users tend to express more themselves by sharing publicly their opinions, emotions and sentiments, the benefits of analyzing such data are eminent, however the process of extracting and transforming these raw data can be a very challenging task particularly when the sentiments are expressed in Arabic language. Two main categories of Arabic are massively used in social networks, namely the modern standard Arabic, which is the official language, and the dialectal Arabic, which is itself, subdivided to several categories depending on countries and regions. In this paper, we focus on analyzing Facebook comments that are expressed in modern standard or in Moroccan dialectal Arabic; therefore we put these two language categories under the scope by testing and comparing two approaches. The first one is the classical approach that considers all Arabic text as homogeneous. The second one, that we propose, require a text classification beforehand sentiment classification, based on language categories: the standard and the dialectal Arabic. The idea behind this approach is to adapt the text preprocessing on each language category with more precision. In supervised classification, we have applied two of the most reputed classifiers in sentiment analysis applications, Naive Bayes and SVM. The results of this study are promising since good performance were obtained.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Al-Kabi, M.N., Abdulla, N.A., Al-Ayyoub, M.: An analytical study of Arabic sentiments: Maktoob case study. In: 2013 8th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 89–94. IEEE (2013) Al-Kabi, M.N., Abdulla, N.A., Al-Ayyoub, M.: An analytical study of Arabic sentiments: Maktoob case study. In: 2013 8th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 89–94. IEEE (2013)
2.
Zurück zum Zitat Al-Sabbagh, R., Girju, R.: Yadac: yet another dialectal arabic corpus. In: LREC, pp. 2882–2889 (2012) Al-Sabbagh, R., Girju, R.: Yadac: yet another dialectal arabic corpus. In: LREC, pp. 2882–2889 (2012)
4.
Zurück zum Zitat Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003) Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, pp. 519–528. ACM (2003)
5.
Zurück zum Zitat Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12) (2009) Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12) (2009)
6.
Zurück zum Zitat Habash, N.Y.: Introduction to arabic natural language processing. Synth. Lect. Hum. Lang. Technol. 3(1), 1–187 (2010)CrossRef Habash, N.Y.: Introduction to arabic natural language processing. Synth. Lect. Hum. Lang. Technol. 3(1), 1–187 (2010)CrossRef
7.
Zurück zum Zitat Khalil, T., Halaby, A., Hammad, M., El-Beltagy, S.R.: Which configuration works best? An experimental study on supervised Arabic twitter sentiment analysis. In: 2015 First International Conference on Arabic Computational Linguistics (ACLing), pp. 86–93. IEEE (2015) Khalil, T., Halaby, A., Hammad, M., El-Beltagy, S.R.: Which configuration works best? An experimental study on supervised Arabic twitter sentiment analysis. In: 2015 First International Conference on Arabic Computational Linguistics (ACLing), pp. 86–93. IEEE (2015)
8.
Zurück zum Zitat Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRef Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)CrossRef
9.
Zurück zum Zitat Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, New York (2015) Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, New York (2015)
10.
Zurück zum Zitat Medhaffar, S., Bougares, F., Esteve, Y., Hadrich-Belguith, L.: Sentiment analysis of Tunisian dialects: linguistic resources and experiments. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 55–61 (2017) Medhaffar, S., Bougares, F., Esteve, Y., Hadrich-Belguith, L.: Sentiment analysis of Tunisian dialects: linguistic resources and experiments. In: Proceedings of the Third Arabic Natural Language Processing Workshop, pp. 55–61 (2017)
11.
Zurück zum Zitat Nabil, M., Aly, M., Atiya, A.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2515–2519 (2015) Nabil, M., Aly, M., Atiya, A.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2515–2519 (2015)
12.
Zurück zum Zitat Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002) Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
13.
Zurück zum Zitat Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef
14.
Zurück zum Zitat Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
15.
Zurück zum Zitat Pozzi, F.A., Fersini, E., Messina, E., Liu, B.: Sentiment Analysis in Social Networks. Morgan Kaufmann, San Francisco (2016) Pozzi, F.A., Fersini, E., Messina, E., Liu, B.: Sentiment Analysis in Social Networks. Morgan Kaufmann, San Francisco (2016)
16.
Zurück zum Zitat Refaee, E., Rieser, V.: An Arabic twitter corpus for subjectivity and sentiment analysis. In: LREC, pp. 2268–2273 (2014) Refaee, E., Rieser, V.: An Arabic twitter corpus for subjectivity and sentiment analysis. In: LREC, pp. 2268–2273 (2014)
17.
Zurück zum Zitat Salamah, J.B., Elkhlifi, A.: Microblogging opinion mining approach for Kuwaiti dialect. In: The International Conference on Computing Technology and Information Management (ICCTIM2014), pp. 388–396. The Society of Digital Information and Wireless Communication (2014) Salamah, J.B., Elkhlifi, A.: Microblogging opinion mining approach for Kuwaiti dialect. In: The International Conference on Computing Technology and Information Management (ICCTIM2014), pp. 388–396. The Society of Digital Information and Wireless Communication (2014)
18.
Zurück zum Zitat Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)CrossRef Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)CrossRef
19.
Zurück zum Zitat Shoukry, A., Rafea, A.: Preprocessing Egyptian dialect tweets for sentiment mining. In: The Fourth Workshop on Computational Approaches to Arabic Script-based Languages, p. 47 (2012) Shoukry, A., Rafea, A.: Preprocessing Egyptian dialect tweets for sentiment mining. In: The Fourth Workshop on Computational Approaches to Arabic Script-based Languages, p. 47 (2012)
20.
Zurück zum Zitat Zaidan, O.F., Callison-Burch, C.: Arabic dialect identification. Comput. Linguist. 40(1), 171–202 (2014)CrossRef Zaidan, O.F., Callison-Burch, C.: Arabic dialect identification. Comput. Linguist. 40(1), 171–202 (2014)CrossRef
Metadaten
Titel
Standard and Dialectal Arabic Text Classification for Sentiment Analysis
verfasst von
Mohcine Maghfour
Abdeljalil Elouardighi
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00856-7_18