research-article

A Machine Learning Approach For Classifying Sentiments in Arabic tweets

Authors:

Rihab Bouchlaghem,

Aymen Elkhelifi,

Rim FaizAuthors Info & Claims

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

Article No.: 24, Pages 1 - 6

https://doi.org/10.1145/2912845.2912874

Published: 13 June 2016 Publication History

Abstract

Nowadays, sentiment analysis methods become more and more popular especially with the proliferation of social media platform users number. In the same context, this paper presents a sentiment analysis approach which can faithfully translate the sentimental orientation of Arabic Twitter posts, based on a novel data representation and machine learning techniques. The proposed approach applied a wide range of features: lexical, surface-form, syntactic, etc. We also made use of lexicon features inferred from two Arabic sentiment words lexicons. To build our supervised sentiment analysis system, we use several standard classification methods (Support Vector Machines, K-Nearest Neighbour, Naïve Bayes, Decision Trees, Random Forest) known by their effectiveness over such classification issues.

In our study, Support Vector Machines classifier outperforms other supervised algorithms in Arabic Twitter sentiment analysis. Via an ablation experiments, we show the positive impact of lexicon based features on providing higher prediction performance.

References

[1]

Hatzivassiloglou, V., McKeown, K. R.: Predicting the semantic orientation of adjectives. In: ACL'97, pp. 174--181. Madrid, Spain (1997)

Digital Library

[2]

Turney, P. D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: ACL'02. Philadelphia (2002)

Digital Library

[3]

Bethard, S., Yu, H., Thornton, A., Hatzivassiloglou, V., Jurafsky, D.: Automatic extraction of opinion propositions and their holders. In: the Association for the Advancement of Artificial Intelligence(AAAI-04), San Jose, California (2004)

[4]

Wilson, T., Wiebe, J., Hwa, R. Just how mad are you? Finding strong and weak opinion clauses. Proc. In: the Association for the Advancement of Artificial Intelligence(AAAI-04), San Jose, California (2004)

Digital Library

[5]

Wiebe, J., Riloff, E. Finding Mutual Benefit between Subjectivity Analysis and Information Extraction. In: IEEE Transactions on Affective Computing, Vol. 2, No. 4, pp. 175--191 (2011)

Digital Library

[6]

Ye, Q., Zhang, Z., Law, R. Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. In: Expert Systems with Applications Vol. 36, Issue 3, Part 2, pp. 6527--6535 (2009)

Digital Library

[7]

Maurel, S., Dini, L. Exploration de corpus pour l'analyse de sentiments. In: DEfi Fouille de Textes, Paris, France, pp. 11--23 (2009)

[8]

Vernier, M., Monceaux, L., Daille, B.L. Catégorisation des évaluations dans un corpus de blogs multi-domaine. In: Revue des nouvelles technologies de l'information, pp. 25 (2009)

[9]

Chardon, B., Muller, S., Laurent, D., Pradel, C., Séguéla, P. Chaîne de traitement symbolique pour l'analyse d'opinion - l'analyseur d'opinions de Synapse Développement face à Twitter. Proc. In: DEfi Fouille de Textes, Caen, France (2015)

[10]

Prabowo, R., Thelwall, M. Sentiment analysis: A combined approach. In: Journal of Informetrics, vol. 3, pp. 143--157 (2009)

[11]

Neviarouskaya, A., Prendinger, H., Ishizuka, M. SentiFul: A Lexicon for Sentiment Analysis. In: IEEE Transactions on Affective Computing, Vol. 2, No. 1, pp. 22--36 (2011)

Digital Library

[12]

Kiritchenko, S., Zhu, X., Mohammad, S. M. Sentiment analysis of short informal texts. In: Journal of Artificial Intelligence Research archive. Vol. 50, Issue 1, pp. 723--762 (2014)

Digital Library

[13]

Al-Sabbagh, R., Girju, R. Yadac: Yet another dialectal Arabic corpus. In: the Eight International Conference on Language Resources and Evaluation, Istanbul (2012)

[14]

Abdul-Mageed, M., Diab, M. Awatif: A multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In: the Eight International Conference on Language Resources and Evaluation, Istanbul (2012)

[15]

Mourad, A., Darwish, K. Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs. In: the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistic. pp. 55--64, Atlanta, Georgia (2013)

[16]

Refaee, E., Rieser, V. An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis. In: the 9th International Conference on Language Resources and Evaluation (LREC'14). Reykjavik, Iceland (2014)

[17]

Ibrahim, H. S., Abdou, S. M., Gheith, M. Sentiment analysis for Modern Standard Arabic colloquial. In: International Journal on Natural Language Computing (IJNLC) Vol. 4, No.2 (2015)

[18]

Abbasi, A., Chen, H., Salem, A. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums. In: ACM Transactions on Information Systems, vol. 26, no. 3, article 12. (2008)

Digital Library

[19]

Rushdi-Saleh, M., Martin-Valdivia, M., Ureña-López, L., Perea-Ortega, J. Bilingual Experiments with an Arabic-English Corpus for Opinion Mining. In: Recent Advances in Natural Language, pp. 740--745, Hissar, Bulgaria (2011)

[20]

A. Soliman, H. T., M. Ali, M., Hedar, A. R., Doss, M. M. Mining social networks' Arabic slang comments. In: IADIS European Conference on Data Mining 2013 (ECDM'13), Prague, Czech Republic (2013)

[21]

Bouchlaghem, R., Elkhelifi, A., Faiz, R. Opinion mining in Microblog Texts using machine learning techniques. In: the Knowledge Discovery and Data Analysis (KDDA 2015), Alger's, Algeria (2015)

[22]

Green, S., Manning, C. D. Better Arabic Parsing: Baselines, Evaluations, and Analysis. In: COLING (2010)

Digital Library

[23]

Pang, Bo, Lee L., Vaithyanathan, Shivakumar. Thumbs up? Sentiment Classification using Machine Learning Techniques. In: EMNLP (2002)

Digital Library

[24]

Heiden, S., Magué, Pinceminb, J. P. Txm: Une plateforme logicielle open-source pour la textométrie conception et développement. In JADT 2010, p. 1021--1032 (2010)

[25]

Church, K. W., Hanks, P. Word association norms, mutual information, and lexicography. In: Computational Linguistics. 16 (1): 22--29. (1990)

Digital Library

[26]

Turney, P., Littman, M. L. Measuring praise and criticism: Inference of semantic orientation from association. In: ACM Transactions on Information Systems, 21(4). (2003)

Digital Library

[27]

Hu, M., Liu, B. Mining and summarizing customer reviews. In: the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 168--177 (2004)

Digital Library

[28]

Fellbaum, C., Grabowski, J., Landes, S. Performance and confidence in a semantic annotation task. In: C. FELLBAUM, Ed., WordNet: an electronic lexical database, Language, Speech and Communication, The MIT Press. Cambridge, Massachusetts, chapter 9, pp. 216--237. (1998)

[29]

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H. The WEKA Data Mining Software: An Update. In: SIGKDD Explorations, vol. 11, Issue 1. (2009)

Digital Library

[30]

Morlane-Hondère, F., D'hondt, E. Feature engineering for tweet polarity classification in the 2015 DEFT challenge. In: DEfi Fouille de Textes, Caen, France (2015)

[31]

Yi, J., Nasukawa, Bunescu, R., Niblack, W. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: the 3rd IEEE International Conference on Data Mining (ICDM), pp. 427--434. (2003)

Digital Library

[32]

Bouchlaghem R., Elkhelifi A., Faiz R. Sentiment analysis in Arabic Twitter posts using supervised methods with combined features. Proceedings of the 17th international conference of Computational Linguistics and Intelligent Text Processing, CiCLing 2016, Konya Turkey, (2016).

Cited By

Aladeemy AAlzahrani AAlgarni MAlsubari SAldhyani TDeshmukh SKhalaf OWong WAqburi S(2024)Advancements and Challenges in Arabic Sentiment Analysis: A Decade of Methodologies, Applications, and Resource DevelopmentHeliyon10.1016/j.heliyon.2024.e39786(e39786)Online publication date: Oct-2024
https://doi.org/10.1016/j.heliyon.2024.e39786
Alqurashi T(2023)Arabic Sentiment Analysis for Twitter Data: A Systematic Literature ReviewEngineering, Technology & Applied Science Research10.48084/etasr.566213:2(10292-10300)Online publication date: 2-Apr-2023
https://doi.org/10.48084/etasr.5662
Bzai JAlam FDhafer ABojović MAltowaijri SNiazi IMehmood R(2022)Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry PerspectiveElectronics10.3390/electronics1117267611:17(2676)Online publication date: 26-Aug-2022
https://doi.org/10.3390/electronics11172676
Show More Cited By

A Machine Learning Approach For Classifying Sentiments in Arabic tweets
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

June 2016

309 pages

ISBN:9781450340564

DOI:10.1145/2912845

Copyright © 2016 ACM.

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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Published: 13 June 2016

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WIMS '16

WIMS '16: International Conference on Web Intelligence, Mining and Semantics

June 13 - 15, 2016

Nîmes, France

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WIMS '16 Paper Acceptance Rate 36 of 53 submissions, 68%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Aladeemy AAlzahrani AAlgarni MAlsubari SAldhyani TDeshmukh SKhalaf OWong WAqburi S(2024)Advancements and Challenges in Arabic Sentiment Analysis: A Decade of Methodologies, Applications, and Resource DevelopmentHeliyon10.1016/j.heliyon.2024.e39786(e39786)Online publication date: Oct-2024
https://doi.org/10.1016/j.heliyon.2024.e39786
Alqurashi T(2023)Arabic Sentiment Analysis for Twitter Data: A Systematic Literature ReviewEngineering, Technology & Applied Science Research10.48084/etasr.566213:2(10292-10300)Online publication date: 2-Apr-2023
https://doi.org/10.48084/etasr.5662
Bzai JAlam FDhafer ABojović MAltowaijri SNiazi IMehmood R(2022)Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry PerspectiveElectronics10.3390/electronics1117267611:17(2676)Online publication date: 26-Aug-2022
https://doi.org/10.3390/electronics11172676
Cortis KDavis B(2021)Over a decade of social opinion mining: a systematic reviewArtificial Intelligence Review10.1007/s10462-021-10030-2Online publication date: 25-Jun-2021
https://doi.org/10.1007/s10462-021-10030-2
Huang CJiang WWu JWang G(2020)Personalized Review Recommendation based on Users’ Aspect SentimentACM Transactions on Internet Technology10.1145/341484120:4(1-26)Online publication date: 6-Oct-2020
https://dl.acm.org/doi/10.1145/3414841
Al-Ibrahim FAlzamil Z(2019)Big Data Contextual Analytics Study on Arabic Tweets SummarizationInternational Journal of Knowledge and Systems Science10.4018/IJKSS.201910010210:4(18-34)Online publication date: Oct-2019
https://doi.org/10.4018/IJKSS.2019100102
Badaro GBaly RHajj HEl-Hajj WShaban KHabash NAl-Sallab AHamdi A(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
https://dl.acm.org/doi/10.1145/3295662
Rodrigues AChiplunkar N(2018)Aspect Based Sentiment Analysis on Product Reviews2018 Fourteenth International Conference on Information Processing (ICINPRO)10.1109/ICINPRO43533.2018.9096796(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/ICINPRO43533.2018.9096796
Ashi MSiddiqui MNadeem F(2018)Pre-trained Word Embeddings for Arabic Aspect-Based Sentiment Analysis of Airline TweetsProceedings of the International Conference on Advanced Intelligent Systems and Informatics 201810.1007/978-3-319-99010-1_22(241-251)Online publication date: 29-Aug-2018
https://doi.org/10.1007/978-3-319-99010-1_22
Altowayan AElnagar A(2017)Improving Arabic sentiment analysis with sentiment-specific embeddings2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258460(4314-4320)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258460
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