Arabic Sentiment Analysis using Deep Learning and Machine Learning approaches. | ||
| Journal of Computing and Communication | ||
| Article 2, Volume 3, Issue 2 - Serial Number 1, July 2024, Pages 10-22 PDF (1.56 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jocc.2024.380113 | ||
| Authors | ||
| gawaher soliman hussein* 1; Abdelnasser riad2 | ||
| 1Information systems department , faculty of computers and informatics, zagazig university, cairo,Egypt | ||
| 2aculty of Computer Science, Misr International University Cairo, Egypt | ||
| Abstract | ||
| Sentiment analysis is defined as an analysis of text to determine the sentiment expressed within it. This text emphasizes the significance of sentiment analysis in web mining and data classification, with detailed illustrations on sentiment analysis of the Arabic language. This study proposed a sentiment analysis framework to review the Arabic text. Two textual representations were explored: term frequency-inverse document frequency (TF-IDF) and word embedding via Word2vec. Various methods have been suggested for categorizing sentiments in Arabic text based on a dependable dataset, including Long Short-Term Memory (LSTM), hybrid LSTM-CNN, Convolutional Neural Network (CNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), and Random Forest (RF). The findings indicated that these methods enhanced Accuracy, precision, Recall, and F1-score. The LR and SVM classifiers accomplished the highest Accuracy with 87%, while the other classifiers (LSTM), (CNN-LSTM), (CNN), (MNB), (RF), and (DT) achieved accuracies with 86.41%, 86.10%, 85.26%, 85%, 84% and 81% respectively. | ||
| Keywords | ||
| Sentiment Analysis; Sentiment Classification; Convolutional Neural Networks; Long-short Term Memory networks, Logistic Regression Random Forest | ||
| References | ||
|
[1] Ahmed A. & Nouh E., (2020), "A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications)", Egyptian Informatics Journal 21 (2020) 7–12
[2] Ruba O., Duha A., Esra A., and Osama H., (2021), "Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review", IEEE Access, Vol. 9, pp. 152628-152645
[3] Elnagar, A., Yagi, S., Nassif, A. B., Shahin, I., & Salloum, S. A. (2021). Sentiment analysis in dialectal Arabic: a systematic review. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2021, 407-417.
[4] Elhassan, N.; Varone, G.; Ahmed, R.; Gogate, M.; Dashtipour, K.; Almoamari, H.; El-Affendi, M.A.; Al-Tamimi, B.N.; Albalwy, F.; Hussain, A. Arabic Sentiment Analysis Based on Word Embeddings and Deep Learning. Computers 2023, 12, 126. https://doi.org/10.3390/ computers12060126
[5] Musleh D., Alkhwaja I., Alkhwaja A., Alghamdi M., Abahussain H., Alfawaz F., Min-Allah N. and Abdulqader M., (2023), "Arabic Sentiment Analysis of YouTube Comments: NLP-Based Machine Learning Approaches for Content Evaluation", big data and cognitive computing, 7, 127, pp 1-16: https://www.youtube.com/watch?v=dnK5lqpyEPg
[6] Samah M. Alzanin, Aqil M. Azmi, Hatim A. Aboalsamh, (2022)" Short text classification for Arabic social media tweets", Journal of King Saud University – Computer and Information Sciences
Volume 34, Issue 9, October 2022, Pages 6595-6604
[7] Lamia, M. A., Gawaher, S. H., Nasser, H. A.(2020). A survey on sentiment analysis algorithms and techniques for Arabic textual data. Fusion: Practice and Applications, 2(2), 74-87.
[8] Al-Smadi M., Qawasmeh O., Al-Ayyoub M., Jararweh Y. & Gupta B., (2018-a), "Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews, Journal of Computational Science Vol. 27, pp. 386–393.
[9] Ammar M. and Rania K., (2019), "Deep learning approaches for Arabic sentiment analysis", Social Network Analysis and Mining, 9:52, pp 1-13.
[10] Al-Smadi M., Talafha M., Al-Ayyoub M. & Jararweh Y. (2019), "Using long–term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews, International Journal of Machine Learning and Cybernetics Vol.10, No. 8, pp. 2163–2175.
[11] Abdulhakeem Q., Ahmed S., and Saman H., (2020), "Arabic Sentiment Analysis (ASA) Using Deep Learning Approach", Journal of Engineering, Vol. 26 No. 6, pp 85-93.
[12] Alharbi, A.; Kalkatawi, M.; Taileb, M., (2021), "Arabic Sentiment Analysis Using Deep Learning and Ensemble Methods", Arabian Journal for Science & Engineering, Vol. 46, Issue 9, pp. 8913-8923...
[13] Asma B, and Zouhour N., (2023), "Sentiment analysis classification for text in social media: application to Tunisian dialect", International Journal on Cybernetics & Informatics (IJCI) Vol. 12, No.2, pp.313-326.
[14] Ahmed B. (2023), "Improved Deep Learning Sentiment Analysis for Arabic", Journal of Theoretical and Applied Information Technology, Vol.101. No 3, pp 1251-12-60
[15] Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y. & Qawasmeh, O., (2018-b), "Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels' reviews using morphological, syntactic and semantic features. Information Processing and Management, Vol. 56, No. 2, pp. 308–319.
[16] Elshakankery K, and Ahmed M., "HILATSA: A hybrid Incremental learning approach for Arabic tweets sentiment analysis", Egyptian Informatics Journal, Vol. 20, No. 3, 2019, pp. 163-171.
[17] Gamal D, Alfonse M, El-Horbaty E., and Salem AB, "Twitter benchmark dataset for Arabic sentiment analysis", International Journal of Modern Education and Computer Science, Vol. 11, No. 1, 2019, pp. 33-38.
[18] Alyami, S., Olatunji, S., (2020), "Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset ", Journal of Information and Knowledge Management, Vol. 19, No. 1, pp. 1–13.
[19] Alsalman, H., (2020), "An Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter social media. 3rd International Conference on Computer Applications and Information Security, Riyadh, Saudi Arabia, 19–21 March 2020.
[20] Alharbi, L., Qamar, A., (2021), "Arabic Sentiment Analysis of Eateries' Reviews: Qassim region Case study", 4th National Computing Colleges Conference, NCCC 2021, Taif, Saudi Arabia, 27–28 March 2021.
[21] Govindan, V., & Balakrishnan, V. (2022). A machine learning approach in analyzing the effect of hyperboles using negative sentiment tweets for sarcasm detection. Journal of King Saud University-Computer and Information Sciences, 34(8), 5110-5120.
[22] Musleh, D., Alkhales, T., Almakki, R., Alnajim, S., Almarshad, S., Alhasaniah, R., Aljameel, S. & Almuqhim, A., (2022), "Twitter Arabic sentiment analysis to detect depression using machine learning. Computers, Materials & Continua, Vol 71, No. 2, pp. 3463–3477.
[23] Maria Y. and bdulla A., (2022), "Analysis and Evaluation of Two Feature Selection Algorithms in Improving the Performance of the Sentiment Analysis Model of Arabic Tweets", IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 6, 2022, pp. 705-711. [24] Arabic Companies Reviews For Sentiment Analysis (kaggle.com), retrieved November 2023.
[25] Socher, R., 2014. Recursive deep learning for natural language processing and computer vision Ph.D. thesis. Stanford University.
[26] Kim, Y., 2014. Convolutional neural networks for sentence classification, in. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751.
[27] Lilleberg, J., Zhu, Y., Zhang, Y., 2015. Support vector machines and word2vec for text classification with semantic features, in: IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC'15), pp. 136–140.
[28] Yang, Z.T., Zheng, J., 2016. Research on Chinese text classification based on Word2vec. Proceeding of the Second IEEE International Conference on Computer and Communications (ICCC), 1166–1170.
[29] Almuzaini, H.A., Azmi, A.M., 2020. Impact of Stemming and Word Embedding on Deep Learning-Based Arabic Text Categorization. IEEE Access 8, 127913– 127928.
[30] Mikolov, T., Chen, K., Corrado, G., Dean, J., 2013. Efficient estimation of word representations in vector space, in: First International Conference on Learning Representations (ICLR 2013).
[31] Alomari, K.M., ElSherif, H.M. and Shaalan, K. (2017) 'Arabic tweets sentimental analysis using machine learning', Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Proceedings, Part I, 27–30 June, Springer International Publishing, Arras, France, pp.602–610.
[32] Altowayan, A.A. and Tao, L. (2016) 'Word embeddings for Arabic sentiment analysis', 2016 IEEE International Conference on Big Data (Big Data), December, IEEE, pp.3820–3825.
[33] Nabil, M., Aly, M. and Atiya, A. (2015) 'Astd: Arabic sentiment tweets dataset', Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, September, pp.2515–2519.
[34] Bolbol, N.K. and Maghari, A.Y. (2020) 'Sentiment analysis of Arabic tweets using supervised machine learning', 2020 International Conference on Promising Electronic Technologies (ICPET), December, IEEE, pp.89–93.
[35] Dahou, A., Elaziz, M.A., Zhou, J. and Xiong, S. (2019) 'Arabic sentiment classification using convolutional neural network and differential evolution algorithm', Computational Intelligence and Neuroscience, Vol. 2019.
[36] Alayba, A.M., Palade, V., England, M. and Iqbal, R. (2018) 'A combined CNN and LSTM model for Arabic sentiment analysis', Machine Learning and Knowledge Extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Proceedings, 27–30 August, Hamburg, Germany, pp.179–191, Springer International Publishing. | ||
|
Statistics Article View: 140 PDF Download: 295 |
||
