Arabic sentiment analysis using supervised classification

Proceedings - 2014 International Conference on Future Internet of Things and Cloud, FiCloud 2014

Volumn , Issue , 2014, Pages 579-583

  Duwairi, Rehab M ^a   Qarqaz, Islam ^a  

^a JORDAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Jordan)

Author keywords

Arabic language; opinion mining; sentiment analysis; sentiment classification; text mining

Indexed keywords

LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; SENTIMENT ANALYSIS; SUPPORT VECTOR MACHINES; TEXT MINING;

ARABIC LANGUAGES; K-NEAREST NEIGHBOR CLASSIFIER; LEXICON-BASED; MACHINE LEARNING APPROACHES; OPINION MINING; SENTIMENT CLASSIFICATION; SUPERVISED CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84922569974     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FiCloud.2014.100     Document Type: Conference Paper

References (26)

1. Hassan. S., Yulan. H., and Alani. H., "Semantic sentiment analysis of Twitter." The Semantic Web-ISWC. Springer, pp. 508-524, 2012.
2. Abdul-Mageed. M., Diab. M., and Korayem M., "Subjectivity and sentiment analysis of modern standard Arabic." Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Vol. 2. 2011.
3. Almas Y., and Ahmad K., "A note on extracting sentiments in financial news in English, Arabic & Urdu." The Second Workshop on Computational Approaches to Arabic Script-based Languages. 2007.
4. Abdul-Mageed M., and Diab M., "AWATIF: A multigenre corpus for Modern Standard Arabic subjectivity and sentiment analysis." Proceedings of LREC, Istanbul, Turkey, 2012.
5. Elhawary M. and Elfeky M., "Mining Arabic Business Reviews." Data Mining Workshops (ICDMW), P. 1108-1113, 2010.
6. Pang B., and Lee L. "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts." Proceedings of the 42nd annual meeting on Association for Computational Linguistics. 2004.
7. El-Halees A., "Arabic Opinion Mining Using Combined Classification Approach." Proceedings of the International Arab Conference on Information Technology, ACIT. 2011.
8. Rushdi-Saleh M., Martín-Valdivia M., Urenã-López L. & Perea-Ortega J.M., Bilingual Experiments with an Arabic-English Corpus for Opinion Mining, 2011.
9. Al-Subaihin A., Al-Khalifa H., and Al-Salman A.M., "A proposed sentiment analysis tool for modern arabic using human-based computing." Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services. ACM, 2011.
10. 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.
11. Nasukawa T., and Jeonghee Y., "Sentiment analysis: Capturing favorability using natural language processing." Proceedings of the 2nd international conference on Knowledge capture. ACM, 2003.
12. Duwairi R., Marji R., Shaban N., and Ershaidat S. " Sentiment Analysis." Graduation Project, Jordan University of Science and Technology, Jordan, June 2012.
13. Rao D., and Ravichandran D., "Semi-supervised polarity lexicon induction." Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2009.
14. Dasgupta S., and Ng V., "Mine the easy, classify the hard: a semi-supervised approach to automatic sentiment classification." In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Association for Computational Linguistics, Volume 2, pp. 701-709. 2009.
15. Sindhwani V., and Melville P., "Document-word coregularization for semi-supervised sentiment analysis." 8th IEEE International Conference on Data Mining (ICDM'08), pp. 1025-1030, 2008.
16. Rapidminer, http://rapid-i.com/, last access on 31-Jan-201.
17. Goldberg, Anderwo B., and Zhu X., "Seeing stars when there aren't many stars: graph-based semisupervised learning for sentiment categorization." Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing. Association for Computational Linguistics, 2006.
18. Kumar A., and Sebastian T.M., "Sentiment Analysis on Twitter." IJCSI International Journal of Computer Science, Issue 93, pp. 372-378, 2102.
19. Malouf R, and Mullen.T. "Taking sides: User classification for informal online political discourse." Internet Research 18.2: pp. 177-190, 2008.
20. Glance N., Hurst M., Nigam K., Siegler M., Stockton R., & Tomokiyo T.,"Deriving marketing intelligence from online discussion. In Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery in data mining, pp. 419-428, 2005.
21. Rish I., "An empirical study of the naive Bayes classifier." IJCAI workshop on empirical methods in artificial intelligence. Vol. 3. No. 22. 2001.
22. Wan X., "Co-training for cross-lingual sentiment classification. "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Association for Computational Linguistics, Volume 1. 2009.
23. Tokuhisa R., Kentaro I., and Yuji M., "Emotion classification using massive examples extracted from the web." Proceedings of the 22nd International Conference on Computational Linguistics, Association for Computational Linguistics, Volume 1. 2008.
24. McCallum A., and Nigam K. "A comparison of event models for Naive Bayes text classification." AAAI-98 workshop on learning for text categorization. Vol. 752. 1998.
25. Ye Q., Zhang Z., and Law R., "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches." Expert Systems with Applications, Vol. 36, Issue 3, pp. 6527-6535, 2009.
26. Fung G., and Olvi L.M., "Incremental support vector machine classification." Proceedings of the Second SIAM International Conference on Data Mining, Arlington, Virginia. 2002.