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Role of Emotion icons in Sentiment classification of Arabic Tweets

Published: 15 September 2014 Publication History

Abstract

In recent years, there is enormous increase of data content due to emergence of social media platforms in digital word of internet. The text mining is very important technique to discover the knowledge from unstructured data. Automatic sentiment analysis is one of the important applications of text mining. The sentiment analysis is used to predict the text polarity (positive, negative, and neutral). Furthermore, the most of users using social media such as Twitter use informal language to express their opinions. In this paper, we propose an automatic approach to predict sentiments for informal Arabic language. We chose Arabic tweets as input for our study. We observed through our experiment results that although emotion icons presence in the tweets helps in development of comparatively more accurate classifier, however they play ambiguous role in defining the sentiments of tweets.

References

[1]
L. Albraheem and H. S. Al-Khalifa, "Exploring the problems of sentiment analysis in informal arabic," in Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services, 2012, pp. 415--418.
[2]
A. El-Halees, "Arabic text classification using maximum entropy," Islam. Univ. J., vol. 15, no. 1, pp. 157--167, 2007.
[3]
M. Rushdi- Saleh, M. T. Martín- Valdivia, L. A. Ureña- López, and J. M. Perea- Ortega, "OCA: Opinion corpus for Arabic," J. Am. Soc. Inf. Sci. Technol., vol. 62, no. 10, pp. 2045--2054, 2011.
[4]
A. Shoukry and A. Rafea, "Sentence-level Arabic sentiment analysis," in Collaboration Technologies and Systems (CTS), 2012 International Conference on, 2012, pp. 546--550.
[5]
A. Kumar and T. M. Sebastian, "Sentiment Analysis on Twitter.," Int. J. Comput. Sci. Issues, vol. 9, no. 4, 2012.
[6]
R. Parikh and M. Movassate, "Sentiment analysis of user-generated twitter updates using various classification techniques," CS224N Final Rep., pp. 1--18, 2009.
[7]
A. Go, R. Bhayani, and L. Huang, "Twitter sentiment classification using distant supervision," CS224N Proj. Report, Stanford, pp. 1--12, 2009.
[8]
J. Read, "Using emoticons to reduce dependency in machine learning techniques for sentiment classification," in Proceedings of the ACL Student Research Workshop, 2005, pp. 43--48.
[9]
A. Pak and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining.," LREC, pp. 1320--1326, 2010.
[10]
L. Barbosa and J. Feng, "Robust sentiment detection on twitter from biased and noisy data," in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 36--44.
[11]
A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, "Sentiment analysis of twitter data," in Proceedings of the Workshop on Languages in Social Media, 2011, pp. 30--38.
[12]
R. Colbaugh and K. Glass, "Estimating sentiment orientation in social media for intelligence monitoring and analysis," in Intelligence and Security Informatics (ISI), 2010 IEEE International Conference on, 2010, pp. 135--137.
[13]
A. A. Al-Subaihin, H. S. Al-Khalifa, and A. S. Al-Salman, "A proposed sentiment analysis tool for modern arabic using human-based computing," in Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services, 2011, pp. 543--546.
[14]
M. Abdul-Mageed, M. Diab, and S. Kübler, "SAMAR: Subjectivity and sentiment analysis for Arabic social media," Comput. Speech Lang., vol. 28, no. 1, pp. 20--37, 2014.
[15]
A. E.-D. A. Hamouda and F. E. El-taher, "Sentiment Analyzer for Arabic Comments System.," Int. J. Adv. Comput. Sci. Appl., vol. 4, no. 3, 2013.
[16]
M. Elhawary and M. Elfeky, "Mining Arabic business reviews," in Data Mining Workshops (ICDMW), 2010 IEEE International Conference on, 2010, pp. 1108--1113.
[17]
M. Abdul-Mageed and M. T. Diab, "AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis.," in LREC, 2012, pp. 3907--3914.

Cited By

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  • (2022)Recent developments in information extraction approaches from Arabic tweets on social networking sitesInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2022.09.0189:9(145-152)Online publication date: Sep-2022
  • (2021)Classification of Arabic Tweets: A ReviewElectronics10.3390/electronics1010114310:10(1143)Online publication date: 12-May-2021
  • (2021)Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social mediaJournal of Big Data10.1186/s40537-021-00488-w8:1Online publication date: 2-Jul-2021
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    MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems
    September 2014
    225 pages
    ISBN:9781450327671
    DOI:10.1145/2668260
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 15 September 2014

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    Author Tags

    1. Opinion Mining (OM)
    2. Sentiment analysis
    3. Twitter
    4. informal Arabic

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    View all
    • (2022)Recent developments in information extraction approaches from Arabic tweets on social networking sitesInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2022.09.0189:9(145-152)Online publication date: Sep-2022
    • (2021)Classification of Arabic Tweets: A ReviewElectronics10.3390/electronics1010114310:10(1143)Online publication date: 12-May-2021
    • (2021)Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social mediaJournal of Big Data10.1186/s40537-021-00488-w8:1Online publication date: 2-Jul-2021
    • (2019)A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/329566218:3(1-52)Online publication date: 7-May-2019
    • (2017)Sentiment analysis on social campaign "Swachh Bharat Abhiyan" using unigram methodAI & Society10.1007/s00146-016-0672-532:4(633-645)Online publication date: 1-Nov-2017
    • (2017)A Review on Corpus Annotation for Arabic Sentiment AnalysisSocial Computing and Social Media. Applications and Analytics10.1007/978-3-319-58562-8_17(215-225)Online publication date: 13-May-2017
    • (2015)Arabic Text Mining a Systematic Review of the Published Literature 2002-20142015 International Conference on Cloud Computing (ICCC)10.1109/CLOUDCOMP.2015.7149632(1-7)Online publication date: Apr-2015

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