Sentiment Analysis in Poems in Misurata Sub-dialect
A Sentiment Detection in an Arabic Sub-dialect
Keywords:Misurata, poems, sentiment, ALSA, Arabic
Over the recent decades, there has been a significant increase and development of resources for Arabic natural language processing. This includes the task of exploring Arabic Language Sentiment Analysis (ALSA) from Arabic utterances in both Modern Standard Arabic (MSA) and different Arabic dialects. This study focuses on detecting sentiment in poems written in Misurata Arabic sub-dialect spoken in Misurata, Libya. The tools used to detect sentiment from the dataset are Sklearn as well as Mazajak sentiment tool1. Logistic Regression, Random Forest, Naive Bayes (NB), and Support Vector Machines (SVM) classifiers are used with Sklearn, while the Convolutional Neural Network (CNN) is implemented with Mazajak. The results show that the traditional classifiers score a higher level of accuracy as compared to Mazajak which is built on an algorithm that includes deep learning techniques. More research is suggested to analyze Arabic sub-dialect poetry in order to investigate the aspects that contribute to sentiments in these multi-line texts; for example, the use of figurative language such as metaphors.
Abdul-Mageed M., Kuebler S., and Diab M. (2012). AWATIF: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis [Paper presentation]. LREC Conference, Istanbul, Turkey.
Abdul-Mageed, M., Diab, M., and Kubler, S. (2014). Samar: Subjectivity and sentiment ¨ analysis for Arabic social media. Computer Speech and Language, 28(1), 20-37.
Abdulla, N., Ahmed, N., Shehab, M., and Al-Ayyoub, M. (2013). Arabic sentiment analysis: Lexicon-based and corpus-based [Paper presentation]. AEECT Conference, NJ, USA.
Abu Farha, I. and Magdy, W. (2019). An online Arabic sentiment analyser. Association for Computational Linguistics, 192-198.
Abu Farha, I. and Magdy, W. (2019). Mazajak. [Computer software]. The University of Edinburgh. http://mazajak.inf.ed.ac.uk:8000/
Abu Kwaik, K., Chatzikyriakidis, S., Dobnik, S., Saad, M., and Johansson, R. (2020). An Arabic tweets sentiment analysis dataset (ATSAD) using distant supervision and self training. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools with a Shared Task on Offensive Language Detection, 1-8.
Ahmad, M., Aftab, S., Bashir, M., Hameed, N. (2018). Sentiment analysis using SVM: A systematic literature review. International Journal of Advanced Computer Science and Applications, 9(2), 182-188.
Ahmed, M., Hasan, R., Ali, A., and Mohammed, M. (2019). The classification of the modern Arabic poetry using machine learning. TELKOMNIKA, 17(5), 2667-2674.
Ain, Q., Ali, A., Riaz, A., Noureen, A., Kamran, M., Hayat, B., and Rehman, A. (2017). Sentiment analysis using deep learning techniques: A review. International Journal of Advanced Computer Science and Applications, 8(6), 424-433.
Alayaba, A., Palade, V., England, M., and Iqbal, R. (2018). A combined CNN and LSTM model for Arabic sentiment analysis. Springer International Publishing, 11015, 179-191.
Albayati, A., Al-Araji, A., Ameen, S. (2020). Arabic sentiment analysis (ASA) using deep learning approach. Journal of Engineering, 6(26), 85-93.
Ali, M. (2021). Arabic sentiment analysis about online learning to mitigate covid-19. Journal of Intelligent Systems, 30(1), 524-540.
Al-Amrani, Y., Lazaar, M., Eddine, K., and Kadiri, E. (2018). Sentiment analysis using a hybrid method of support vector machine and decision tree. Journal of Theoretical and Applied Information Technology, 96(7). 1886-1895.
Al-Ayyoub, M., Bani Essa, S., and Alsmadi, I. (2015). Lexicon-based sentiment analysis of Arabic tweets. International Journal of Social Network Mining, 2(2), 101-114.
Al-Ayyoub, M., Khamaiseh, A., Jararweh, Y. and Al-Kabi, M. (2019). A comprehensive survey of Arabic sentiment analysis. Information Processing and Management, 56(2), 320-342.
Al-Azani, S. and El-Alfy, E. (2017). Hybrid deep learning for sentiment polarity determination of Arabic microblogs. Springer International Publishing, 10638, 491-500.
Al-Balushi, R. (2012). Why verbless sentences in Standard Arabic are verbless. The Canadian Journal of Linguistics, 57,1-30.
Al-Humoud, S., Altuwaijri, M., Albuhairi, T., Alohaideb, W. (2015). Survey on Arabic sentiment analysis in Twitter. International Journal of Social, Behavioral, Educational, Economic and Management Engineering, 9, 364-368.
Alhumoud, S., Albuhairi, T. and Altuwaijri, M. (2015). Arabic sentiment analysis using WEKA a hybrid learning approach. Knowledge Discovery, Knowledge Engineering and Knowledge Management,1, 402-408.
Almuqren, L., Alzammam, A., Alotaibi, S., Cristea, A. and Alhumoud, S. (2017). A review on corpus annotation for Arabic sentiment analysis. Springer, 215-225.
Al-Radaideh Q., and Al-Qudah G. (2017). Application of rough set-based feature selection for Arabic sentiment analysis. Cogn. Comput, 9, 436-45.
Alsharif, O., Alshamaa, D., and Ghneim, N. (2013). Emotion classification in Arabic poetry using Machine Learning. International Journal of Computer Applications, 65(16),10-15.
Alshutayri, A., Atwell, E., AlOsaimy, A., Dickins, J., Ingleby, M., and Watson, J. (2016). Arabic language WEKA-based dialect classifier for Arabic automatic speech recognition transcripts. Varieties and Dialects Conference. Osaka, Japan.
Al-Samdi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., and Gupta, B. (2017). Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science, 27, 386-393.
Al-Sallab, A., Baly, R. Hajj, H., Shaban, K., El-Hajj, W., Badaro, G. (2017). AROMA: A recursive deep learning model for opinion mining in Arabic as a low resource language. ACM Journal, 16(4), 1-20.
Alsayat, A. and Elmitwally, N. (2020). A comprehensive study for Arabic sentiment analysis (challenges and applications). Egyptian Informatics Journal, 7-12.
Alsiyat, I. and Piao, S. (2020). Metaphorical expressions in automatic Arabic sentiment analysis. Lancaster University, 1-6.
Antoun, W. Baly, F. and Hajj, H. (2020). AraBERT: Transformer-based model for Arabic language understanding. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, 9-15.
Baali, M., and Ghneim, N. (2019). Emotion analysis of Arabic tweets using deep learning approach. Journal of Big Data, 6(8), 7-12.
Baly, R., Badaro, G., El-Khoury, G., Moukalled, R., Aoun, R., Hajj, H. (2017). A characterization study of Arabic Twitter data with a benchmarking for state-of-the-art opinion mining models. Association for Computational Linguistics, 110-118.
Boudad, N., Faizi, R., Thami, R., and Chiheb, R. (2018). Sentiment analysis in Arabic: A review of the literature. Ain Shams Engineering Journal, 9(4), 2479-2490.
Darwish, K., Magdy, W. (2014). Arabic information retrieval. Foundations and Trends® in Information Retrieval, 7(4), 239-342.
Devi, B., Bai, V., Ramasubbareddy, S., Govinda, K. (2020). Emerging Research in Data Engineering Systems and Computer Communications: Sentiment Analysis on Movie Reviews. P. Venkata Krishna and Mohammad Obaidat.
Elarnaoty, M. (2012). A machine learning approach for opinion holder extraction in Arabic language. International Journal of Artificial Intelligence, 3, 45-63.
Elfaik, H. and Nfaoui, E. (2020). Deep bidirectional LSTM network learning-based sentiment analysis for Arabic text. Journal of Intelligent Systems, 30, 395-412.
El-Halees, A. (2017). Arabic opinion mining using distributed representations of documents. In Proceedings of the 2017 Palestinian International Conference on Information and Communication Technology, 28-33.
Elmasry, M., Soliman, T., and Hedar, A. (2014). Sentiment analysis of Arabic slang comments on Facebook. International Journal of Computers and Technology, 12(5), 3470-3478.
Elramli, Y. (2012). Assimilation in the Phonology of a Libyan Arabic Dialect: A Constraint-based approach. Ph.D. dissertation. Newcastle University.
Galal, M., Madbouly, M., and El-Zoghby, A. (2019). Classifying Arabic text using deep learning. Journal of Theoretical and Applied Information Technology, 97(23), 3412-3422.
Ghallab, A., Mohsen, A., and Ali, Y. (2020). Arabic sentiment analysis: A systematic literature review. Applied Computational Intelligence and So Computing, 2020, 1-21.
Habash, N. (2010). Introduction to Arabic natural language processing. Synthesis Lectures on Human Language Technologies, 3(1), 1-187.
Hammam, M., Elmahdy, A., Halawa, A., and Youness, H. (2018). Improve the automatic classification accuracy for Arabic tweets using ensemble methods. Journal of Electrical Systems and Information Technology, 5(3), 363-370.
Heikal, M., Torki, M., and El-Makky, N. (2018). Sentiment analysis of Arabic Tweets using deep learning. Procedia Computer Science, 142(1), 114-122.
Hemmatian, F., and Sohrabi, M. (2017). A survey on classification techniques for opinion mining and sentiment analysis. Springer, 52(1), 1495-1545.
Ibrahim, H., Abdou, S., and Gheith, M. (2015). Sentiment analysis for modern standard Arabic and colloquial. International Journal on Natural Language Computing,4(2), 95-109.
Jin, H., Zhu,Y., Jin, Z. and Arora, S. (2014). Sentiment visualization on Tweets stream. Journal of Software, 9(9), 2348-2352.
Khasawneh, R. Wahsheh, H. Al Kabi M. and Aismadi, I. (2013) Sentiment analysis of Arabic social media content: A comparative study. Proceedings of the 8th International Conference for Internet Technology and Secured Transactions (ICITST), 101-106.
Liu, B. (2012). Sentiment analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
Maiteq, T. (2020). A Phonetic study of vowel epenthesis in the dialect of Misurata: Preliminary results. Journal of Academic Research (Humanities and Social Science), 16, 1-6.
Mamtesh, M. and Mehla, S. (2019). Sentiment analysis of movie reviews using machine learning classifiers. International Journal of Computer Applications, 182(50), 25-28.
Moussa, M., Mohamed, E., Haggag, M. (2018). A generic lexicon-based framework for sentiment analysis. International Journal Computers and Applications, 42(5), 1-11.
Nabil, M., Aly, M. and Atiya, A. (2015). LABR: A large-scale Arabic sentiment analysis benchmark. Cornell University. 1-9.
Oraby, S., El-Sonbaty, Y., and Abou El-Nasr, M. (2013). Finding opinion strength using rule-based parsing for Arabic sentiment analysis. Springer, 509–520.
Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 10, 79-86. Association for Computational Linguistics.
Pang, B. and Lee, L. (2008). Opinion mining and sentiment analysis. Foundation and Trends in Information Retrieval, 2(1-2), 1-135.
Refaee, E., and Rieser, V. (2014). An Arabic Twitter corpus for subjectivity and sentiment analysis. Association for Computational Linguistics, 2268-2273.
Rosenthal, S., Farra, N., and Nakov, P. (2017). Semeval-2017 task 4: Sentiment analysis in Twitter. Association for Computational Linguistics, 502-518.
Shoeb, M., and Ahmed, J. (2017). Sentiment analysis and classification of tweets using data mining. International Research Journal of Engineering and Technology, 4(12), 1471-1474.
Shoukry, A. and Rafea, A. (2012). Sentence level Arabic sentiment analysis. Proceedings of the International Conference on Collaboration Technologies and Systems. Denver, USA. 546-550.
Theelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.
The Encyclopedia Britannica (2020). Retrieved April 17, 2021, from https://www.britannica.com/place/Misurata
WEKA. (2014) WEKA 3: Data Mining Software in Java, [Computer software] The University of Waikato. https://www.cs.waikato.ac.nz/ml/weka/
Zahidi, Y., Younoussi, Y. and Al-Amrani, Y. (2020). A powerful comparison of deep learning frameworks for Arabic sentiment analysis. International Journal of Electrical and Computer Engineering (IJECE), 11(1), 745-752.
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