Iraqi Sentiment and Emotion Analysis Using Deep Learning
Main Article Content
Abstract
Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) collected from Facebook are used to evaluate the model. Experiments showed that the model obtained good results, as the accuracy of the model was 91.1, 92.4, and 92.5% for IADS, ACMID, and IAD, respectively. The results of the model outperformed previous works for all datasets.
Article Details
Section
How to Cite
References
Abdullah, M., Hadzikadicy, M., and Shaikhz, S., 2018. SEDAT: sentiment and emotion detection in Arabic text using cnn-lstm deep learning. Proceedings - 17th IEEE International Conference on Machine Learning and Applications ICMLA, Orlando, FL, USA, 17-20 Dec, 2018, pp. 835–840.
Doi:10.1109/ICMLA.2018.00134
Abdullah, M., and Shaikh, S., 2018. TeamUNCC at semeval-2018 task 1: emotion detection in English and Arabic tweets using deep learning. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop. New Orleans, Louisiana. Association for Computational Linguistics, June 2018, pp. 350–357. Doi:10.18653/v1/s18-1053
Abu Kwaik, K., Saad, M., Chatzikyriakidis, S., and Dobnik, S., 2019. LSTM-CNN deep learning model for sentiment analysis of dialectal Arabic. Communications in Computer and Information Science, 1108, pp. 108–121. Doi:10.1007/978-3-030-32959-4_8
Alayba, A.M., Palade, V., England, M., and Iqbal, R. 2018a. A combined CNN and LSTM model for Arabic sentiment analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS 11015, pp. 179–191. Doi:10.1007/978-3-319-99740-7_12
Alayba, A.M., Palade, V., England, M., and Iqbal, R., 2018b. Improving sentiment analysis in Arabic using word representation. 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), London, UK, 12-14 March 2018, pp. 13–18. Doi:10.1109/ASAR.2018.8480191
Almahdawi, A.J., and Teahan, W.J., 2019. A new Arabic dataset for emotion recognition. Advances in Intelligent Systems and Computing, 998, pp. 200–216. Doi:10.1007/978-3-030-22868-2_16
Alnawas, A., and Arici, N., 2019. Sentiment analysis of Iraqi Arabic dialect on Facebook based on distributed representations of documents. ACM Transactions on Asian and Low-Resource Language Information Processing, 18(3), pp. 1-17. Doi:10.1145/3278605
Alswaidan, N., and Menai, M.E.B., 2020. Hybrid feature model for emotion recognition in Arabic text. IEEE Access, 8, pp. 37843–37854. Doi:10.1109/ACCESS.2020.2975906
Askar, A.K.A.J., and Sjarif, N.N. A., 2021. Annotated corpus of mesopotamian-Iraqi dialect for sentiment analysis in social media. International Journal of Advanced Computer Science and Applications (IJACSA), 12(4), pp. 101–105. Doi:10.14569/IJACSA.2021.0120413
Baali, M., and Ghneim, N., 2019. Emotion analysis of Arabic tweets using deep learning approach. Journal of Big Data, 6(1), P. 89. Doi:10.1186/s40537-019-0252-x
Badaro, G., El Jundi, O., Khaddaj, A., Maarouf, A., Kain, R., Hajj, H., and El-Hajj, W., 2018. EMA at semeval-2018 task 1: Emotion mining for Arabic. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018- Proceedings of the 12th Workshop, New Orleans, Louisiana. Association for Computational Linguistics, June 2018, pp. 236–244. Doi:10.18653/v1/s18-1036
Ekman, P., 1992. An argument for basic emotions. Cognition and Emotion, 6(3–4), pp. 169–200. Doi:10.1080/02699939208411068
Elzayady, H., Badran, K.M., and Salama, G. I., 2020. Arabic opinion mining using combined CNN - LSTM models. International Journal of Intelligent Systems and Applications, 12(4), pp. 25–36. Doi:10.5815/ijisa.2020.04.03
Heikal, M., Torki, M., and El-Makky, N., 2018. Sentiment analysis of Arabic tweets using deep learning. Procedia Computer Science, 142, pp. 114–122. Doi:10.1016/j.procs.2018.10.466
Khabour, S.M., Al-Radaideh, Q.A., and Mustafa, D., 2022. A new ontology-based method for Arabic sentiment analysis. Big Data and Cognitive Computing, 6(2), P. 48. Doi:10.3390/bdcc6020048.
Khalil, E.A.H., El Houby, E.M.F., and Mohamed, H.K., 2021. Deep learning for emotion analysis in Arabic tweets. Journal of Big Data, 8(1), p. 136. Doi:10.1186/s40537-021-00523-w
Mansy, A., Rady, S., and Gharib, T., 2022. An ensemble deep learning approach for emotion detection in Arabic tweets. International Journal of Advanced Computer Science and Applications, 13(4). Doi:10.14569/ijacsa.2022.01304112
Medhat, W., Hassan, A., and Korashy, H., 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), pp. 1093–1113. Doi:10.1016/j.asej.2014.04.011
Mohammad, S.M., Bravo-Marquez, F., Salameh, M., and Kiritchenko, S., 2018. Semeval-2018 task 1: affect in tweets. NAACL HLT 2018 - International Workshop on Semantic Evaluation, SemEval 2018 - Proceedings of the 12th Workshop, New Orleans, Louisiana. Association for Computational Linguistics, June 2018, pp. 1–17. Doi:10.18653/v1/s18-1001
Mohammed, A., and Kora, R., 2019. Deep learning approaches for Arabic sentiment analysis. Social Network Analysis and Mining, 9(1), pp. 1–12. Doi:10.1007/s13278-019-0596-4
Nassif, A.B., Darya, A.M., and Elnagar, A., 2021. Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, 21(1), pp. 1-25. Doi:10.1145/3466171
Ombabi, A.H., Ouarda, W., and Alimi, A.M., 2020. Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks. Social Network Analysis and Mining, 10(1), p. 53. Doi:10.1007/s13278-020-00668-1
Oueslati, O., Cambria, E., HajHmida, M. Ben, and Ounelli, H., 2020. A review of sentiment analysis research in the Arabic language. Future Generation Computer Systems, 112(1), pp. 408–430. Doi:10.1016/j.future.2020.05.034
Plutchik, R., 1980. A general psychoevolutionary theory of emotion. In Theories of Emotion, pp. 3–33. Doi:10.1016/b978-0-12-558701-3.50007-7
Plutchik, R., 1994. The psychology and biology of emotion. HarperCollins College Publishers.
Sailunaz, K., and Alhajj, R., 2019. Emotion and sentiment analysis from Twitter text. Journal of Computational Science, 36, p. 101003. Doi:10.1016/j.jocs.2019.05.009
Saleh, H., Mostafa, S., Alharbi, A., El-Sappagh, S. and Alkhalifah, T., 2022. Heterogeneous ensemble deep learning model for enhanced Arabic sentiment analysis. Sensors, 22(10), p. 3707. Doi:10.3390/s22103707
Soliman, A.B., Eissa, K., and El-Beltagy, S.R., 2017. Aravec: a set of Arabic word embedding models for use in Arabic NLP. Procedia Computer Science, 117, pp. 256–265. Doi:10.1016/j.procs.2017.10.117
Abdulhameed, T.Z., 2020. Cross language information transfer between modern standard Arabic and its dialects – a framework for automatic speech. Western Michigan University. https://scholarworks.wmich.edu/dissertations
Wint, Z.Z., Manabe, Y., and Aritsugi, M., 2018. Deep learning based sentiment classification in social network services datasets. Proceedings - 2018 IEEE/ACIS 3rd International Conference on Big Data, Cloud Computing, Data Science and Engineering, BCD 2018, Yonago, Japan, 12-13 July 2018, pp. 91–96. Doi:10.1109/BCD2018.2018.00022
Word2vec Embeddings. https://radimrehurek.com/gensim/models/word2vec.html