Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

  • W. M. S. Yafooz Computer Science Department, Taibah University, Saudi Arabia
  • E. A. Hizam Information Systems Department, Taibah University, Saudi Arabia
  • W. A. Alromema Information Systems Department, Taibah University, Saudi Arabia

Abstract

Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.

Keywords: sentiment analysis, machine learning, classification, ensemble methods

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References

J. R. Saura, P. Palos-Sanchez, and A. Grilo, "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining," Sustainability, vol. 11, no. 3, Jan. 2019, Art. no. 917. https://doi.org/10.3390/su11030917

M. Govindarajan, "Sentiment analysis of restaurant reviews using hybrid classification method," in Proceedings of 2nd IRF International Conference, Chennai, India, Feb. 2014, pp. 127-133.

S. Rani and P. Kumar, "A Sentiment Analysis System to Improve Teaching and Learning," Computer, vol. 50, no. 5, pp. 36-43, May 2017. https://doi.org/10.1109/MC.2017.133

A. Salinca, "Business Reviews Classification Using Sentiment Analysis," in 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, Sep. 2015, pp. 247-250. https://doi.org/10.1109/SYNASC.2015.46

U. P. Gurav and S. Kotrappa, "Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6356-6361, Oct. 2020. https://doi.org/10.48084/etasr.3805

J. Carrillo-de-Albornoz, J. R. Vidal, and L. Plaza, "Feature engineering for sentiment analysis in e-health forums," PLOS ONE, vol. 13, no. 11, 2018, Art. no. e0207996. https://doi.org/10.1371/journal.pone.0207996

M. Madhukar and S. Verma, "Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2014-2016, Oct. 2017. https://doi.org/10.48084/etasr.1246

O. Oyebode, F. Alqahtani, and R. Orji, "Using Machine Learning and Thematic Analysis Methods to Evaluate Mental Health Apps Based on User Reviews," IEEE Access, vol. 8, pp. 111141-111158, 2020. https://doi.org/10.1109/ACCESS.2020.3002176

S. Angelidis and M. Lapata, "Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis," Transactions of the Association for Computational Linguistics, vol. 6, pp. 17-31, Aug. 2018. https://doi.org/10.1162/tacl_a_00002

Z. Wang, C. S. Chong, L. Lan, Y. Yang, S. B. Ho, and J. C. Tong, "Fine-grained sentiment analysis of social media with emotion sensing," in 2016 Future Technologies Conference (FTC), Dec. 2016, pp. 1361-1364. https://doi.org/10.1109/FTC.2016.7821783

J. Luo, S. Huang, and R. Wang, "A fine-grained sentiment analysis of online guest reviews of economy hotels in China," Journal of Hospitality Marketing & Management, vol. 30, no. 1, pp. 71-95, Jan. 2021. https://doi.org/10.1080/19368623.2020.1772163

C. Yang, H. Zhang, B. Jiang, and K. Li, "Aspect-based sentiment analysis with alternating coattention networks," Information Processing & Management, vol. 56, no. 3, pp. 463-478, May 2019. https://doi.org/10.1016/j.ipm.2018.12.004

M. Song, H. Park, and K. Shin, "Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean," Information Processing & Management, vol. 56, no. 3, pp. 637-653, May 2019. https://doi.org/10.1016/j.ipm.2018.12.005

W. Xue and T. Li, "Aspect Based Sentiment Analysis with Gated Convolutional Networks," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, Jul. 2018, vol. 1, pp. 2514-2523. https://doi.org/10.18653/v1/P18-1234

F. F. Balahadia, M. C. G. Fernando, and I. C. Juanatas, "Teacher's performance evaluation tool using opinion mining with sentiment analysis," in 2016 IEEE Region 10 Symposium (TENSYMP), May 2016, pp. 95-98. https://doi.org/10.1109/TENCONSpring.2016.7519384

I. A. Kandhro et al., "Sentiment Analysis of Students' Comment by using Long-Short Term Model," Indian Journal of Science and Technology, vol. 12, no. 8, pp. 1-16, Feb. 2019. https://doi.org/10.17485/ijst/2019/v12i8/141741

I. A. Kandhro, S. Z. Jumani, F. Ali, Z. U. Shaikh, M. A. Arain, and A. A. Shaikh, "Performance Analysis of Hyperparameters on a Sentiment Analysis Model," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6016-6020, Aug. 2020. https://doi.org/10.48084/etasr.3549

E. M. Clark et al., "A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter," arXiv e-prints, vol. 1805, p. arXiv:1805.09959, May 2018.

M. Zolnoori et al., "Mining news media for understanding public health concerns," Journal of Clinical and Translational Science, pp. 1-10, Oct. 2019. https://doi.org/10.1017/cts.2019.434

F. Saeed, W. M.S. Yafooz, M. Al-Sarem, and E. A. Hezzam, "Detecting Health-Related Rumors on Twitter using Machine Learning Methods," International Journal of Advanced Computer Science and Applications, vol. 11, no. 8, 2020. https://doi.org/10.14569/IJACSA.2020.0110842

A. Al-Alimi, E. Halboub, A. K. Al-Sharabi, T. Taiyeb-Ali, N. Jaafar, and N. N. Al-Hebshi, "Independent determinants of periodontitis in Yemeni adults: A case-control study," International Journal of Dental Hygiene, vol. 16, no. 4, pp. 503-511, 2018. https://doi.org/10.1111/idh.12352

M. Hijazi, H. Jentsch, J. Al-Sanabani, M. Tawfik, and T. W. Remmerbach, "Clinical and cytological study of the oral mucosa of smoking and non-smoking qat chewers in Yemen," Clinical Oral Investigations, vol. 20, no. 4, pp. 771-779, May 2016. https://doi.org/10.1007/s00784-015-1569-2

M. A. Al-Duais and Y. S. Al-Awthan, "Association between qat chewing and dyslipidaemia among young males," Journal of Taibah University Medical Sciences, vol. 14, no. 6, pp. 538-546, Dec. 2019. https://doi.org/10.1016/j.jtumed.2019.09.008

B. Kalakonda, S. A. Al-Maweri, H.-M. Al-Shamiri, A. Ijaz, S. Gamal, and E. Dhaifullah, "Is Khat (Catha edulis) chewing a risk factor for periodontal diseases? A systematic review," Journal of Clinical and Experimental Dentistry, vol. 9, no. 10, pp. e1264-e1270, Oct. 2017. https://doi.org/10.4317/jced.54163

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