Evaluation of Naïve Bayes Classification in Arabic Short Text Classification

Mohammed Fadhil Ibrahim, Mahdi Ahmed Alhakeem, Nawar Abbood Fadhil

Abstract


In the last few years, and due to the vast widespread of social media applications, texts have become more important and get more attention. Since texts, in general, are carrying a lot of information that can be extracted and analyzed. Several researchers have done significant works in text classification. Within different scripts such as English and other western languages, several challenges and obstacles have been addressed with such a field of research. Regarding the Arabic language, the process is different from other vital languages since Arabic is considered an orthographic language that depends on the word shape. It is not easy to apply the standard text preprocessing techniques since it affects the word meaning. This paper evaluates Arabic short text classification using three standard Naïve Bayes classifiers. In our method, we classify the thesis and dissertations using their titles to perform the classification process. The collected dataset is collected from different repositories by using standard scrapping techniques. Our method classifies the document based on their titles to be placed in the desired specialization. Several preprocessing techniques have been applied, such as (punctuation removal, stop words removal, and space vectorization). For feature extraction, we adopt the TF-IDF method. We implemented three types of Naïve Bayes classifiers which are (Multinomial Naïve Bayes, Complemented Naïve Bayes, and Gaussian Naïve Bayes). The study results showed that Complemented Naïve Bayes Classifier proposed the best performance with (0.84) of accuracy for the testing phase. The results of the study are promising to be applied with different short text classifications.


Keywords


Arabic Text Classification, Multinomial Naïve Bayes, Complemented Naïve Bayes, Gaussian Naïve Bayes , TF-IDF

Full Text:

PDF

References


A. Elnagar, R. Al-Debsi, and O. Einea, "Arabic text classification using deep learning models," Information Processing & Management, vol. 57, no. 1, p. 102121, 2020.

CrossRef

H.-F. Yu, C.-H. Ho, P. Arunachalam, M. Somaiya, and C.-J. Lin, "Product title classification versus text classification," Csie. Ntu. Edu. Tw, pp. 1-25, 2012.

Y.-C. Lin, A. Datta, and G. Di Fabbrizio, "E-commerce product query classification using implicit user's feedback from clicks," in 2018 IEEE International Conference on Big Data (Big Data), 2018: IEEE, pp. 1955-1959.

CrossRef | PubMed

M. Skinner and S. Kallumadi, "E-commerce Query Classification Using Product Taxonomy Mapping: A Transfer Learning Approach," in eCOM@ SIGIR, 2019.

N. Bel, J. Diz-Pico, M. Marimon, and J. Pocostales, "Classifying short texts for a Social Media monitoring system," Procesamiento del Lenguaje Natural, no. 59, pp. 57-64, 2017.

J. Al Qundus, A. Paschke, S. Gupta, A. M. Alzouby, and M. Yousef, "Exploring the impact of short-text complexity and structure on its quality in social media," Journal of Enterprise Information Management, 2020.

CrossRef

Z. Alzamil, D. Appelbaum, and R. Nehmer, "An ontological artifact for classifying social media: Text mining analysis for financial data," International Journal of Accounting Information Systems, vol. 38, p. 100469, 2020.

CrossRef

S. Ma, X. Sun, J. Lin, and X. Ren, "A hierarchical end-to-end model for jointly improving text summarization and sentiment classification," arXiv preprint arXiv:1805.01089, 2018.

CrossRef

A. Abdi, S. M. Shamsuddin, S. Hasan, and J. Piran, "Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion," Information Processing & Management, vol. 56, no. 4, pp. 1245-1259, 2019.

CrossRef

T. Baumel, J. Nassour-Kassis, R. Cohen, M. Elhadad, and N. Elhadad, "Multi-label classification of patient notes a case study on ICD code assignment," arXiv preprint arXiv:1709.09587, 2017.

A. Blanco, A. Casillas, A. Pérez, and A. D. de Ilarraza, "Multi-label clinical document classification: Impact of label-density," Expert Systems with Applications, vol. 138, p. 112835, 2019.

CrossRef

K. Tayal, R. Nikhil, S. Agarwal, and K. Subbian, "Short text classification using graph convolutional network," in NIPS workshop on Graph Representation Learning, 2019.

K. Kowsari, K. Jafari Meimandi, M. Heidarysafa, S. Mendu, L. Barnes, and D. Brown, "Text classification algorithms: A survey," Information, vol. 10, no. 4, p. 150, 2019.

CrossRef

A. Ghallab, A. Mohsen, and Y. Ali, "Arabic Sentiment Analysis: A Systematic Literature Review," Applied Computational Intelligence and Soft Computing, vol. 2020, p. 7403128, 2020/01/29 2020, doi: 10.1155/2020/7403128.

CrossRef

N. Al-Twairesh, H. Al-Khalifa, and A. Al-Salman, "Subjectivity and sentiment analysis of Arabic: trends and challenges," in 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), 2014: IEEE, pp. 148-155.

CrossRef

Wikipedia. "Arabic." Wikimedia Foundation. https://en.wikipedia.org/wiki/Arabic (accessed April 02, 2021).

S. Clerides, P. Davis, and A. Michis, "National sentiment and consumer choice: The Iraq war and sales of US products in Arab countries," The Scandinavian Journal of Economics, vol. 117, no. 3, pp. 829-851, 2015.

CrossRef

I. W. Stats. "Top Ten Internet Languages in The World - Internet Statistics." https://www.internetworldstats.com/stats7.htm (accessed April 02, 2021.

W. Zaghouani, "Critical survey of the freely available Arabic corpora," arXiv preprint arXiv:1702.07835, 2017.

T. Pranckevičius and V. Marcinkevičius, "Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification," Baltic Journal of Modern Computing, vol. 5, no. 2, p. 221, 2017.

CrossRef

B. Trstenjak, S. Mikac, and D. Donko, "KNN with TF-IDF based framework for text categorization," Procedia Engineering, vol. 69, pp. 1356-1364, 2014.

CrossRef

T. Al-Moslmi, M. Albared, A. Al-Shabi, N. Omar, and S. Abdullah, "Arabic senti-lexicon: Constructing publicly available language resources for Arabic sentiment analysis," Journal of information science, vol. 44, no. 3, pp. 345-362, 2018.

CrossRef

T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent trends in deep learning based natural language processing," ieee Computational intelligenCe magazine, vol. 13, no. 3, pp. 55-75, 2018.

CrossRef

M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y. Jararweh, and B. Gupta, "Deep learning for Arabic NLP: A survey," Journal of computational science, vol. 26, pp. 522-531, 2018.

CrossRef

I. Hmeidi, M. Al-Ayyoub, N. A. Abdulla, A. A. Almodawar, R. Abooraig, and N. A. Mahyoub, "Automatic Arabic text categorization: A comprehensive comparative study," Journal of Information Science, vol. 41, no. 1, pp. 114-124, 2015.

CrossRef

M. Al-Ayyoub, A. A. Khamaiseh, Y. Jararweh, and M. N. Al-Kabi, "A comprehensive survey of arabic sentiment analysis," Information Processing & Management, vol. 56, no. 2, pp. 320-342, 2019/03/01/ 2019, doi: https://doi.org/10.1016/j.ipm.2018.07.006.

CrossRef

M. N. Al-Kabi, Q. A. Al-Radaideh, and K. W. Akkawi, "Benchmarking and assessing the performance of Arabic stemmers," Journal of Information Science, vol. 37, no. 2, pp. 111-119, 2011.

CrossRef

R. Duwairi and M. El-Orfali, "A study of the effects of preprocessing strategies on sentiment analysis for Arabic text," Journal of Information Science, vol. 40, no. 4, pp. 501-513, 2014.

CrossRef

A. El Mahdaouy, E. Gaussier, and S. O. El Alaoui, "Arabic text classification based on word and document embeddings," in International Conference on Advanced Intelligent Systems and Informatics, 2016: Springer, pp. 32-41.

CrossRef

H. Wang and M. Hong, "Supervised Hebb rule based feature selection for text classification," Information Processing & Management, vol. 56, no. 1, pp. 167-191, 2019.

CrossRef

A. K. Uysal and S. Gunal, "The impact of preprocessing on text classification," Information Processing & Management, vol. 50, no. 1, pp. 104-112, 2014.

CrossRef

N. A. Ahmed, M. A. Shehab, M. Al-Ayyoub, and I. Hmeidi, "Scalable multi-label arabic text classification," in 2015 6th International Conference on Information and Communication Systems (ICICS), 2015: IEEE, pp. 212-217.

CrossRef

A. Y. Taha and S. Tiun, "BINARY RELEVANCE (BR) METHOD CLASSIFIER OF MULTI-LABEL CLASSIFICATION FOR ARABIC TEXT," Journal of Theoretical & Applied Information Technology, vol. 84, no. 3, 2016.

M. A. Shehab, O. Badarneh, M. Al-Ayyoub, and Y. Jararweh, "A supervised approach for multi-label classification of Arabic news articles," in 2016 7th International Conference on Computer Science and Information Technology (CSIT), 2016: IEEE, pp. 1-6.

CrossRef | PubMed

I. Hmeidi, M. Al-Ayyoub, N. A. Mahyoub, and M. A. Shehab, "A lexicon based approach for classifying Arabic multi-labeled text," International Journal of Web Information Systems, 2016.

CrossRef

B. Al-Salemi, S. A. M. Noah, and M. J. Ab Aziz, "RFBoost: an improved multi-label boosting algorithm and its application to text categorisation," Knowledge-Based Systems, vol. 103, pp. 104-117, 2016.

CrossRef

B. Al-Salemi, M. Ayob, and S. A. M. Noah, "Feature ranking for enhancing boosting-based multi-label text categorization," Expert Systems with Applications, vol. 113, pp. 531-543, 2018.

CrossRef

G. R. Biradar, J. Raagini, A. Varier, and M. Sudhir, "Classification of Book Genres using Book Cover and Title," in 2019 IEEE International Conference on Intelligent Systems and Green Technology (ICISGT), 2019: IEEE, pp. 72-723.

CrossRef

S. Bahassine, A. Madani, M. Al-Sarem, and M. Kissi, "Feature selection using an improved Chi-square for Arabic text classification," Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 2, pp. 225-231, 2020.

CrossRef

H. Chantar, M. Mafarja, H. Alsawalqah, A. A. Heidari, I. Aljarah, and H. Faris, "Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification," Neural Computing and Applications, vol. 32, no. 16, pp. 12201-12220, 2020.

CrossRef

D. AbuZeina and F. S. Al-Anzi, "Employing fisher discriminant analysis for Arabic text classification," Computers & Electrical Engineering, vol. 66, pp. 474-486, 2018.

CrossRef

I. A. Doush, F. Alkhateeb, and A. Albsoul, "AraDaisy: A system for automatic generation of Arabic DAISY books," International Journal of Computer Applications in Technology, vol. 55, no. 4, pp. 322-333, 2017.

CrossRef

M. Sayed, R. K. Salem, and A. E. Khder, "A survey of Arabic text classification approaches," International Journal of Computer Applications in Technology, vol. 59, no. 3, pp. 236-251, 2019.

CrossRef

A. K. Sangaiah, A. E. Fakhry, M. Abdel-Basset, and I. El-henawy, "Arabic text clustering using improved clustering algorithms with dimensionality reduction," Cluster Computing, vol. 22, no. 2, pp. 4535-4549, 2019.

CrossRef

J. Ferrero, D. Schwab, and H. Cherroun, "Word embedding-based approaches for measuring semantic similarity of arabic-english sentences," in International Conference on Arabic Language Processing, 2017: Springer, pp. 19-33.

CrossRef

S.-W. Kim and J.-M. Gil, "Research paper classification systems based on TF-IDF and LDA schemes," Human-centric Computing and Information Sciences, vol. 9, no. 1, p. 30, 2019/08/26 2019, doi: 10.1186/s13673-019-0192-7.

CrossRef

L. Havrlant and V. Kreinovich, "A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and variations motivated by this explanation)," International Journal of General Systems, vol. 46, no. 1, pp. 27-36, 2017.

CrossRef

B. Das and S. Chakraborty, "An improved text sentiment classification model using TF-IDF and next word negation," arXiv preprint arXiv:1806.06407, 2018.




DOI: http://dx.doi.org/10.23851/mjs.v32i4.994

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Al-Mustansiriyah Journal of Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Copyright (c) 2018 by Al-Mustansiriyah Journal of Science
ISSN: 1814-635X (Print), ISSN: 2521-3520 (online)