3,121
Views
2
CrossRef citations to date
0
Altmetric
Articles

A two-level deep learning approach for emotion recognition in Arabic news headlines

, , &
Pages 604-613 | Received 10 Jul 2020, Accepted 10 Nov 2020, Published online: 25 Nov 2020

References

  • Griffiths F, Dobermann T, Cave J, et al. The impact of online social networks on health and health systems: a scoping review and case studies. Policy Internet. 2015;7(4):473–496.
  • Bessière K, Pressman S, Kiesler S, et al. Effects of internet use on health and depression: a longitudinal study. J Med Internet Res. 2010;12(1):e6.
  • Johnston WM, Davey GC. The psychological impact of negative TV news bulletins: The catastrophizing of personal worries. Br J Psychol. 1997;88(1):85–91.
  • McHugo GJ, Smith CA, Lanzetta JT. The structure of self-reports of emotional responses to film segments. Motiv Emot. 1982;6(4):365–385.
  • Sternbach RA. Assessing differential autonomic patterns in emotions. J Psychosom Res. 1962;6(2):87–91.
  • Mathews A. Why worry? The cognitive function of anxiety. Behav Res Ther. 1990;28(6):455–468.
  • Mathews A, MacLeod C. Cognitive approaches to emotion and emotional disorders. Annu Rev Psychol. 1994;45:25–50.
  • Mogg K, Mathews A, Eysenck M. Attentional bias to threat in clinical anxiety states. Cogn Emot. 1992;6(2):149–159.
  • Wells A, Morrison AP. Qualitative dimensions of normal worry and normal obsessions: a comparative study. Behav Res Ther. 1994;32(8):867–870.
  • Ibrahim HS, Abdou SM, Gheith M. MIKA: A tagged corpus for modern standard Arabic and colloquial sentiment analysis. In 2015 IEEE 2nd International Conference on Recent Trends in Information Systems, ReTIS 2015 – Proceedings; 2015. p. 353–358. Available from: https://doi.org/https://doi.org/10.1109/ReTIS.2015.7232904
  • Philippot P. Inducing and assessing differentiated emotion-feeling states in the laboratory. Cogn Emot. 1993;7(2):171–193.
  • Gibbs W, McKendrick J. Contemporary research methods and data analytics in the news industry. Hershey (PA): IGI Global; 2015.
  • Hamouda AEA, El-taher FE. Sentiment analyzer for Arabic comments system. Int J Adv Computer Sci Appl. 2013;4(3):99–103.
  • Al-Ayyoub M, Bani Essa S, Alsmadi I. Lexicon-based sentiment analysis of Arabic tweets. Int J Soc Netw Min. 2015;2(2):101–114.
  • Khoja S, Garside R. Stemming Arabic Text. Lancaster, UK: Computing Department, Lancaster University; 1999.
  • Abuaiadh D. Dataset for Arabic Document Classification [online]. 2011. Available from: http://diab.edublogs.org/dataset-for-arabic-document-classification/
  • Diab M. Second generation AMIRA tools for Arabic processing: Fast and robust tokenization, POS tagging, and base phrase chunking. Proceedings of the Second International Conference on Arabic Language Resources and Tools; Cairo, Egypt: 2009. p. 285–288.
  • Abdul-Mageed M, Kuebler S, Diab M. Samar: A system for subjectivity and sentiment analysis of arabic social media. Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics; 2012. p. 19–28.
  • Al-Kabi MN, Khasawneh RT, Wahsheh HA. Evaluating social context in Arabic opinion mining. Int Arab J Inform Technol. 2018;15(6):974–982.
  • Al-Azani S, El-Alfy E-SM. Imbalanced sentiment polarity detection using emoji-based features and bagging ensemble. Proceedings of the 2018 1st International Conference on Computer Applications and Information Security (ICCAIS); Riyadh, Saudi Arabia: IEEE; 2018. p. 1–5.
  • Elouardighi A, Maghfour M, Hammia H, et al. A machine learning approach for sentiment analysis in the standard or dialectal Arabic facebook comments. Proceedings of the 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech); Rabat, Morocco: IEEE; 2017. p. 1–8.
  • Al-Tamimi A-K, Shatnawi A, Bani-Issa E. Arabic sentiment analysis of youtube comments. Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT); Amman, Jordan: IEEE; 2017. p. 1–6.
  • El Ariss O, Alnemer LM. Morphology based Arabic sentiment analysis of book reviews. Proceedings of the International Conference on Computational Linguistics and Intelligent Text Processing; Budapest, Hungary: Springer; 2017. p. 115–128.
  • Maghfour M, Elouardighi A. Standard and dialectal Arabic text classification for sentiment analysis. Proceedings of the International Conference on Model and Data Engineering; Marrakesh, Morocco: Springer; 2018. p. 282–291.
  • Nuseir A, Al-Ayyoub M, Al-Kabi M. Improved hierarchical classifiers for multi-way sentiment analysis. International Arab Conference on Information Technology (ACIT'2016) 2016.
  • Altawaier MM, Tiun S. Comparison of machine learning approaches on Arabic twitter sentiment analysis. Int J Adv Sci Eng Inform Technol. 2016;6(6):1067–1073.
  • Socher R, Pennington J, Huang EH, et al. Semi-supervised recursive autoencoders for predicting sentiment distributions. Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics; 2011. p. 151–161.
  • Socher R, Lin CCY, Ng AY, et al. Parsing Natural Scenes and Natural Language with Recursive Neural Networks. ICML; 2011.
  • Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank. Proceedings of the 2013 conference on empirical methods in natural language processing; 2013. p. 1631–1642.
  • Cliche M. BB twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs. 2017. Preprint arXiv:1704.06125.
  • Maas L, Daly RE, Pham PT, et al. Learning accurate, compact, and interpretable tree annotation. Proceedings of ACL; 2011.
  • Baly R, Badaro G, El-Khoury G, et al. A characterization study of Arabic twitter data with a benchmarking for state-of-the-art opinion mining models. Proceedings of the Third Arabic Natural Language Processing Workshop; 2017. p. 110–118.
  • Al Sallab A, Hajj H, Badaro G, et al. Deep learning models for sentiment analysis in Arabic. Proceedings of the Second Workshop on Arabic Natural Language Processing; 2015. p. 9–17.
  • Ekman P. An argument for basic emotions. Cogn Emot. 1992;6:169–200.
  • Chaffar S, ElSayed H, Belhouari S, et al. Arabic news headlines. IEEE Dataport. 2020; Available from: https://doi.org/http://doi.org/10.21227/7e79-nt12
  • Alarabiya RSS service. (n.d.). [updated 2017 Jun 12]. Available from: https://www.alarabiya.net/tools/mrss
  • Al-Riyadh RSS service. (n.d.). [updated 2017 May 7]. Available from: http://www.alriyadh.com/page/rss
  • Boudelaa S. Is the Arabic mental lexicon morpheme-based or stem-based? Implications for spoken and written word recognition. Handbook of Arabic literacy. Springer; 2014. p. 31–54.
  • Moftah M, Fakhr W, Abdou S, et al. Stem-based Arabic language models experiments. Proceedings of the Second International Conference on Arabic Language Resources and Tools, Cairo, Egypt, April. The MEDAR Consortium; 2009.
  • Aghtar S. A New Incremental Classification Approach Monitoring: The Risk of Heart Disease. 2012. Available from: https://macsphere.mcmaster.ca/bitstream/11375/12637/1/fulltext.pdf.
  • Ramaswamy S, Golub TR. DNA microarrays in clinical oncology. Clin Oncol. 2002;20:1932–1941.
  • Zhao Z, Liu H. Spectral feature selection for supervised and unsupervised learning. Proceedings of the 24th international conference on Machine learning; ACM; 2007. p. 1151–1157.
  • Lakshmi Devasena C, Proficiency comparison of ZeroR, RIDOR and PART classifiers for Intelligent Heart Disease Prediction. Oper Res Appl: Int J Adv in Computer Sci Technol (IJACST). 2014;3(11):12–18. Special Issue.
  • Elsayed H, Syed L. An automatic early risk classification of hard coronary heart diseases using Framingham scoring model. In second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017); ACM; 2017.
  • Hamming RW. Error detecting and error correcting codes. Bell Syst Tech J. 1950;29(2):147–160. DOI: https://doi.org/10.1002/j.1538-7305.1950.tb00463.x, MR 0035935.html?cref=navdesk
  • Xing Y, Wang J, Zhao Z. Combination data mining methods with new medical data to predicting outcome of coronary heart disease. In 2007 International Conference on Convergence Information Technology; IEEE; 2007. p. 868–872.
  • Calders T, Verwer S. Three naive Bayes approaches for discrimination-free classification. Data Min Knowl Discov. 2010;21(2):277–292. Available from: https://doi.org/https://doi.org/10.1007/s10618-010-0190-x
  • Ani R, Augustine A, Akhil NC, et al. Random forest ensemble classifier to predict the coronary heart disease using risk factors. In Proceedings of the International Conference on Soft Computing Systems; India: Springer; 2016. p. 701–710.
  • Evgeniou T, Pontil M. Support vector machines: theory and applications. Machine learning and its applications. 2001. p. 249–257.
  • Kasi HG. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. DOI:https://doi.org/10.1145/3065386