Asian Journal of Information Technology

Year: 2019
Volume: 18
Issue: 1
Page No. 20 - 27

Arabic Semantic Classifier of Arabic Social Media "Twitter" Users

Authors : Jamal Alnaeb, Issam Salman and Mohamad Bassam Kurdy

References

Alabbas, W., H.M. Al-Khateeb, A. Mansour, G. Epiphaniou and I. Frommholz, 2017. Classification of colloquial Arabic tweets in real-time to detect high-risk floods. Proceedings of the 2017 International Conference on Social Media, Wearable And Web Analytics (Social Media), June 9-20, 2017, IEEE, London, UK., ISBN:978-1-5090-5058-1, pp: 1-8.

Alabdullatif, A., B. Shahzad and E. Alwagait, 2016. Classification of arabic twitter users: A study based on user behaviour and interests. Mob. Inf. Syst., 2016: 1-11.
CrossRef  |  Direct Link  |  

Allen, D. and A. Darwiche, 2013. Optimal Time-Space Trade off in Probabilistic Inference. In: Advances in Bayesian Networks, Gamez, J.A., S. Moral and A. Salmeron (Eds.). Springer, New York, USA., ISBN:9783540398790, pp: 39-55.

Barbosa, L. and J. Feng, 2010. Robust sentiment detection on twitter from biased and noisy data. Proceedings of the 23rd International Conference on Computational Linguistics: Posters, August 2010, Stroudsburg, PA., pp: 36-44.

Daood, A., I. Salman and N. Ghneim, 2017. Comparison study of automatic classifiers performance in emotion recognition of Arabic social media users. J. Theoret. Applied Inform. Technol., 95: 5173-5183.
Direct Link  |  

Do, H.J. and H.J. Choi, 2015. Korean twitter emotion classification using automatically built emotion lexicons and fine-grained features. Proceedings of the 29th International Pacific Asia Conference on Language, Information and Computation: Posters (PACLIC 29), October 30-November 1, 2015, Deparment of Computer Science and Engineering Shanghai Jiao Tong University, Shanghai, China, pp: 142-150.

Mishra, A.K. and B.K. Ratha, 2016. Study of random tree and random forest data mining algorithms for microarray data analysis. Intl. J. Adv. Electr. Comput. Eng., 3: 1-7.
Direct Link  |  

Mohsen, A.M., H.A. Hassan and A.M. Idrees, 2016. Documents emotions classification model based on TF-IDF weighting measure. Intl. Scholarly Sci. Res. Innovation, 10: 252-258.
Direct Link  |  

Moskovitch, R., N. Nissim, D. Stopel, C. Feher and R. Englert et al., 2007. Improving the detection of unknown computer worms activity using active learning. Proceedings of the Annual International Conference on Artificial Intelligence, September 10-13, 2007, Springer, Berlin, Heidelberg, ISBN:978-3-540-74564-8, pp: 489-493.

Mozina, M., J. Demsar, M. Kattan and B. Zupan, 2004. Nomograms for visualization of naive Bayesian classifier. Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, September 20-24, 2004, Pisa, Italy, pp: 337-348.

Platt, J., 2000. Fast training of Support Vector Machine using Sequential minimal optimization. Microsoft Way Redmond, Redmond, Washington, USA.

Toepfer, M., P. Kluegl, A. Hotho and F. Puppe, 2010. Conditional random fields for local adaptive reference extraction. Department of Computer Science, Wurzburg, Germany.

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