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Articles

Emotions extraction from Arabic tweets

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Pages 661-675 | Received 08 Jun 2017, Accepted 22 May 2018, Published online: 07 Jun 2018

References

  • Shukla A, Shukla S. A survey on sentiment classification and analysis using data mining. Int J Adv Res Comput Sci. 2015;6(7):20–24.
  • Assiri A, Emam A, Aldossari H. Arabic sentiment analysis: A survey. Int J Adv Comput Sci Appl. 2015;6(12):75–85.
  • Hasan M, Rundensteiner E, Agu E. EMOTEX: Detecting Emotions in Twitter Messages. ASE BIGDATA/SOCIALCOM/CYBERSECURITY Conf.; 2014. p. 27–31.
  • Al-A'abed M, Al-Ayyoub M. A Lexicon – Based approach for emotion analysis of Arabic social media content. In: Proceedings of the International Computer Sciences and Informatics Conference (ICSIC 2016). Amman, Jordan: Amman Arab University; 2016.
  • Jain MC, Kulkarni VY. Texemo: conveying emotion from text-The study. Int J Comput Appl. 2014;86(4):975–8887.
  • Kumari R, Sasane M. Emotion analysis using text mining on social networks. Int J Innov Res Technol. 2015;2(1):2349–6002.
  • Shelke NM, Indira Gandhi P. Approaches of emotion detection from text. ISSN. 2014;2(2):123–128.
  • Wikarsa L, Thahir SN. A text mining application of emotion classifications of Twitter’s users using Naive Bayes method. In: Proceeding of 2015 1st International Conference on Wireless and Telematics, ICWT 2015; 2016.
  • Vijay Gaikwad S, Patil PDY, Patil P. Text mining methods and techniques. Int J Comput Appl. 2014;85(17):975–8887.
  • Gupta V, Lehal GS. A survey of text mining techniques and applications. J Emerg Technol Web Intell. 2009;1(1):60–76.
  • Mac Kim S. Recognising emotions and sentiments in text [Ph.D. thesis]. University of Sydney; 2011.
  • Dhawan S, Singh K, Sehrawat D. Emotion mining techniques in social networking sites. Int J Inf Comput Technol. 2014;4(12):1145–1153.
  • Shahraki AG. Emotion Mining from Text; 2015.
  • Shelke NM. Approaches of emotion detection from text. Int J Comput Sci Inf Technol Res. 2014;2(2):123–128.
  • Appel O, Chiclana F, Carter J. Main concepts, state of the art and future research questions in sentiment analysis. Acta Polytech Hungarica. 2015;12(3):87–108.
  • Pak A, Paroubek P. Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10); LREC. Valletta (Malta): European Languages Resources Association (ELRA).
  • Vaghela VB, Jadav BM, Scholar ME. Analysis of various sentiment classification techniques. Int J Comput Appl. 2016;140(3):975–8887.
  • Giachanou A, Crestani F. 28 like it or not: A survey of twitter sentiment analysis methods. ACM Comput. Survey. 2016;49(28).
  • Chen S, Pedrycz W. Sentiment analysis and ontology engineering: An environment of computational intelligence. Cham, Switzerland: Springer International Publishing; 2016.
  • Suzuki Y, Takamura H, Okumura M. Application of semi-supervised learning to evaluative expression classification. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2006;3878 LNCS:502–513.
  • Joshi NS, Itkat SA. A survey on feature level sentiment analysis. IJCSIT. 2014;5(4):5422–5425.
  • Mudinas A, Zhang D, Levene M. Combining lexicon and learning based approaches for concept-level sentiment analysis. Proc First Int Work Issues Sentim Discov Opin Min – WISDOM. 2012;12:1–8.
  • Turney PD. Thumbs up or thumbs down? Semantic Orientation applied to Unsupervised Classification of Reviews. Proc. 40th Annu. Meet. Assoc. Comput. Linguist.; July; Philadelphia, Pennsylvania. Ithaca (NY): Cornell University Library; 2002. p. 417–424
  • Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. Empir Methods Nat Lang Process. 2002;10(July):79–86.
  • Koppel M, Schler J. The importance of neutral examples for learning sentiment. Comput Intell. 2006;22(2):100–109. doi: 10.1111/j.1467-8640.2006.00276.x
  • Rabab’Ah AM, Al-Ayyoub M, Jararweh Y, and Al-Kabi MN. Evaluating SentiStrength for Arabic Sentiment Analysis. 7th International Conference on Computer Science and Information Technology; 13–14 July 2016. DOI:10.1109/CSIT.2016.7549458
  • Al-humoud SO. Arabic Sentiment Analysis using WEKA a Hybrid Learning Approach Arabic Sentiment Analysis using WEKA a Hybrid Learning. no. November; 2015 .
  • Aldayel HK, Azmi AM. Arabic tweets sentiment analysis – a hybrid scheme. J Inf Sci. 2016;42(6):782–797. doi: 10.1177/0165551515610513
  • Duwairi RM, Qarqaz I. Arabic Sentiment Analysis using Supervised Classification. 2014 International Conference on Future Internet of Things and Cloud (FiCloud); 27–29 Aug. 2014; Barcelona, Spain. https://doi.org/10.1109/FiCloud.2014.100 DOI:10.1109/FiCloud.2014.100
  • Gievska S, Koroveshovski K, Chavdarova T. A Hybrid Approach for Emotion Detection in Support of Affective Interaction. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, August 23–24, 2014. p. 428–432.
  • Rabie O, Sturm C. Feel the heat: Emotion detection in Arabic social media content. Int. Conf. Data Mining, Internet Comput. Big Data. 2014; pp. 37–49.
  • Sivic J, Zisserman A. Video Google: A text retrieval approach to object matching in videos. In Proc. ICCV; 2003.
  • Joachims T. Text categorization with support vector machines: Learning with many relevant features. Berlin Heidelberg: Springer; 1998. p. 137–142.
  • Platt J. Sequential minimal optimization: A fast algorithm for training support vector machines; 1998. Available from: https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/
  • Murphy KP. Naive Bayes classifiers. Vancouver (Canada): University of British Columbia; 2006.
  • Rajput A, et al. J48 and JRIP rules for e-governance data. Int J Comput Sci Security. 2011;5(2):201.
  • Larkey LS, Ballesteros L, Connell ME. Light Stemming for Arabic Information Retrieval.
  • Fan R-E, Chang K-W, Hsieh C-J, et al. LIBLINEAR: A library for large linear classification. J Mach Learn Res. 2008;9:1871–1874.

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