[1] C. Zhang and Y. Lu, “Study on artificial intelligence: The state of the art and future prospects,” J. Ind. Inf. Integr., vol. 23, p. 100224, 2021, doi: 10.1016/j.jii.2021.100224.
[2] D.W. Otter, J.R. Medina, and J.K. Kalita, “A survey of the usages of deep learning for natural language processing,” IEEE Trans. Neural Networks Learn. Syst., vol. 32, no. 2, pp. 604-624, 2021, doi: 10.1109/TNNLS.2020.2979670.
[3] A. Khosravi and H. Abdolhosseini, “Personality in social networks using thematic modelling of user feedback,” Soft Comput. J., vol. 11, no. 2, pp. 50-61, 2023, doi: 10.22052/scj.2023.243197.1006 [In Persian].
[4] F. Pourgholamali, M. Kahani, and E. Asgarian, “Exploiting Big Data Technology for Opinion Mining,” Soft Comput. J., vol. 9, no. 1, pp. 26-39, 2020, doi: 10.22052/scj.2021.111450 [In Persian].
[5] M. Birjali, M. Kasri, and A.B. Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowl. Based Syst., vol. 226, p. 107134, 2021, doi: 10.1016/j.knosys.2021.107134.
[6] F.A. Acheampong, C. Wenyu, and H. Nunoo-Mensah, “Text-based emotion detection: Advances, challenges, and opportunities,” Eng. Rep., vol. 2, no. 7, p. e12189, 2020, doi: 10.1002/eng2.12189.
[7] S. Kusal, S. Patil, K. Kotecha, R. Aluvalu, and V. Varadarajan, “AI based emotion detection for textual big data: Techniques and contribution,” Big Data Cong. Comput., vol. 5, no. 3, p. 43, 2021, doi: 10.3390/bdcc5030043.
[8] B. Kratzwald, S. Ilic, M. Kraus, S. Feuerriegel, and H. Prendinger, “Deep learning for affective computing: Text-based emotion recognition in decision support,” Decis. Support Syst., vol. 115, pp. 24-35, 2018, doi: 10.1016/j.dss.2018.09.002.
[9] A. Dzedzickis, A. Kaklauskas, and V. Bucinskas, “Human emotion recognition: Review of sensors and methods,” Sensors, vol. 20, no. 3, p. 592, 2020, doi: 10.3390/s20030592.
[10] M. Schreiner, T. Fischer, and R. Riedl, “Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda,” Electron. Commer. Res., vol. 21, no. 2, pp. 329-345, 2021, doi: 10.1007/s10660-019-09353-8.
[11] P. Ekman and D. Cordaro, “What is meant by calling emotions basic,” Emotion Rev., vol. 3, no. 4, pp. 364-370, 2011, doi: 10.1177/1754073911410740.
[12] P. Ekman, “An argument for basic emotions,” Cogn. Emotion, vol. 6, no. 3-4, pp. 169-200, 1992, doi: 10.1080/02699939208411068.
[13] R.E. Jack, W. Sun, I. Delis, O.G.B. Garrod, and P.G. Schyns, “Four not six: Revealing culturally common facial expressions of emotion,” J. Exp. Psychol. Gen., vol. 145, no. 6, pp. 708-730, 2016, doi: 10.1037/xge0000162.
[14] S. Zad, M. Heidari, J.H. Jones Jr., and O. Uzuner, “Emotion detection of textual data: An interdisciplinary survey,” in IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA, 2021, pp. 255-261, doi: 10.1109/AIIoT52608.2021.9454192.
[15] R. Plutchik, “In search of the basic emotions,” PsycCRITIQUES, vol. 29, no. 6, pp. 511-513, 1984.
[16] E.C.-C. Kao, C.-C. Liu, T.-H. Yang, C.-T. Hsieh, and V.-W. Soo, “Towards text-based emotion detection a survey and possible improvements,” in Int. Conf. Inf. Manag. Eng., Kuala Lumpur, Malaysia, 2009, pp. 70-74. doi: 10.1109/ICIME.2009.113.
[17] N. Sabri, R. Akhavan, and B. Bahrak, “EmoPars: A collection of 30k emotion-annotated Persian social media texts,” in Proc. Student Res. Worksh. Assoc. RANLP 2021, 2021, pp. 167-173.
[18] B. Karimi, (2022, Apr. 03), Persian tweets emotional dataset, [Online]. Available: https://www.kaggle.com/behdadkarimi/persian-tweets-emotional-dataset.
[19] H. Mirzaee, J. Peymanfard, H.H. Moshtaghin, and H. Zeinali, “ArmanEmo: A Persian dataset for text-based emotion detection,” arXiv, CoRR abs/2207.11808, 2022, doi: 10.48550/arXiv.2207.11808.
[20] N. Sabri, (2022, Jan. 31), Persian-Emotion-Detection, [Online]. Available: https://github.com/nazaninsbr/Persian-Emotion-Detection.
[21] Arman Rayan Sharif, (2023, Jan. 06), ArmanEmo, [Online]. Available: https://github.com/Arman-Rayan-Sharif/arman-text-emotion.
[22] F. Zare Mehrjardi, M. Yazdian-Dehkordi, and A. Latif, “Evaluating classical machine learning and deep-learning methods in sentiment analysis of Persian telegram message,” Soft Comput. J., vol. 11, no. 1, pp. 88-105, 2022, doi: 10.22052/scj.2023.246553.1077 [In Persian].
[23] M. Feizi-Derakhshi, Z. Mottaghinia, and M. Asgari-Chenaghlu, “Persian Text Classification Based on Deep Neural Networks,” Soft Comput. J., vol. 11, no. 1, pp. 120-139, 2022, doi: 10.22052/scj.2023.243182.1010 [In Persian].
[24] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in 1st Int. Conf. Learn. Represent. (ICLR), Scottsdale, Arizona, USA, 2013, Workshop Track Proceedings.
[25] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Trans. Assoc. Computat. Linguistics, vol. 5, pp. 135-146, 2017, doi: 10.1162/tacl_a_00051.
[26] J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,” in Proc. Conf. Empir. Methods Nat. Lang. Process. (EMNLP), Doha, Qatar: Association for Computational Linguistics, 2014, pp. 1532-1543. doi: 10.3115/v1/D14-1162.
[27] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. Conf. North American Chapter Assoc. Comput. Linguis. Hum. Lang. Technol. (NAACL-HLT), Minneapolis, MN, USA, 2019, pp. 4171-4186, doi: 10.18653/v1/n19-1423.
[28] K. Clark, U. Khandelwal, O. Levy, and C.D. Manning, “What does BERT look at? An analysis of BERT’s attention,” in Proc. ACL Worksh. BlackboxNLP: Analyzing Interp. Neural Netw. NLP, Florence, Italy: Association for Computational Linguistics, 2019, pp. 276-286. doi: 10.18653/v1/W19-4828.
[29] A. Vaswani et al., “Attention is all you need,” arXiv, CoRR abs/1706.03762, 2017, doi: 10.48550/arXiv.1706.03762.
[30] E. Batbaatar, M. Li, and K.H. Ryu, “Semantic-emotion neural network for emotion recognition from text,” IEEE Access, vol. 7, pp. 111866-111878, 2019, doi: 10.1109/ACCESS.2019.2934529.
[31] A. Chatterjee, U. Gupta, M.K. Chinnakotla, R. Srikanth, M. Galley, and P. Agrawal, “Understanding emotions in text using deep learning and big data,” Comput. Hum. Behav., vol. 93, pp. 309-317, 2019, doi: 10.1016/j.chb.2018.12.029.
[32] T.T. Sasidhar, P.B, and S.K. P, “Emotion detection in Hinglish (Hindi+English) code-mixed social media text,” Proc. Comput. Sci., vol. 171, pp. 1346-1352, 2020, doi: 10.1016/j.procs.2020.04.144.
[33] M. Abdullah, M. Hadzikadicy, and S. Shaikhz, “SEDAT: Sentiment and emotion detection in Arabic text using CNN-LSTM deep learning,” in 17th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), Orlando, FL: IEEE, 2018, pp. 835-840, doi: 10.1109/ICMLA.2018.00134.
[34] X. Sun, C. Zhang, and L. Li, “Dynamic emotion modelling and anomaly detection in conversation based on emotional transition tensor,” Inf. Fusion, vol. 46, pp. 11-22, 2019, doi: 10.1016/j.inffus.2018.04.001.
[35] M. Baali and N. Ghneim, “Emotion analysis of Arabic tweets using deep learning approach,” J. Big Data, vol. 6, p. 89, 2019, doi: 10.1186/s40537-019-0252-x.
[36] W. Ragheb, J. Aze, S. Bringay, and M. Servajean, “Attention-based modeling for emotion detection and classification in textual conversations,” arXiv, CoRR abs/1906.07020, 2019.
[37] K. Shrivastava, S. Kumar, and D.K. Jain, “An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network,” Multim. Tools Appl., vol. 78, no. 20, pp. 29607-29639, 2019, doi: 10.1007/s11042-019-07813-9.
[38] A.F. Adoma, N.-M. Henry, W. Chen, and N. Rubungo Andre, “Recognizing emotions from texts using a bert-based approach,” in 17th Int. Comput. Conf. Wavelet Active Media Technol. Inf. Process. (ICCWAMTIP), Chengdu, China: IEEE, 2020, pp. 62-66, doi: 10.1109/ICCWAMTIP51612.2020.9317523.
[39] H. Al-Omari, M.A. Abdullah, and S. Shaikh, “EmoDet2: Emotion detection in English textual dialogue using BERT and BiLSTM models,” in 11th Int. Conf. Inf. Commun. Syst. (ICICS), Irbid, Jordan: IEEE, 2020, pp. 226-232. doi: 10.1109/ICICS49469.2020.239539.
[40] S. Qu, Y. Yang, and Q. Que, “Emotion classification for spanish with XLM-RoBERTa and TextCNN,” in CEUR Workshop Proceedings, Malaga, Spain, September, 2021, pp. 94-100.
[41] A. Das, O. Sharif, M.M. Hoque, and I.H. Sarker, “Emotion classification in a resource constrained language using transformer-based approach,” in Proc. Conf. North American Chapter Assoc. Comput. Linguis. Student Res. Worksh. (NAACL-HLT), Online, 2021, pp. 150-158, doi: 10.18653/v1/2021.naacl-srw.19.
[42] K. Dheeraj and T. Ramakrishnudu, “Negative emotions detection on online mental-health related patients texts using the deep learning with MHA-BCNN model,” Expert Syst. Appl., vol. 182, p. 115265, 2021, doi: 10.1016/j.eswa.2021.115265.
[43] Y. Fu, L. Guo, L. Wang, Z. Liu, J. Liu, and J. Dang, “A sentiment similarity-oriented attention model with multi-task learning for text-based emotion recognition,” in MultiMedia Modeling - Proc. 27th Int. Conf. (MMM), Prague, Czech Republic, 2021, Part I, pp. 278–289, doi: 10.1007/978-3-030-67832-6_23.
[44] X.-M. Lin, C.-H. Ho, L.-T. Xia, and R.-Y. Zhao, “Sentiment analysis of low-carbon travel APP user comments based on deep learning,” Sustain. Energy Technol. Assess., vol. 44, p. 101014, 2021, doi: 10.1016/j.seta.2021.101014.
[45] H. Luo, “Emotion detection for spanish with data augmentation and transformer-based models,” in CEUR Workshop Proceedings, Malaga, Spain, September, 2021, pp. 35-42.
[46] H.Q. Abonizio, E.C. Paraiso, and S. Barbon Junior, “Toward text data augmentation for sentiment analysis,” IEEE Trans. Artif. Intell., vol. 3, no. 5, pp. 657-668, 2022, doi: 10.1109/TAI.2021.3114390.
[47] J. Luo, M. Bouazizi, and T. Ohtsuki, “Data augmentation for sentiment analysis using sentence compression-based SeqGAN with data screening,” IEEE Access, vol. 9, pp. 99922-99931, 2021, doi: 10.1109/ACCESS.2021.3094023.
[48] P. Kumar and B. Raman, “A BERT based dual-channel explainable text emotion recognition system,” Neural Networks, vol. 150, pp. 392-407, 2022, doi: 10.1016/j.neunet.2022.03.017.
[49] A. Majeed, M.O. Beg, U. Arshad, and H. Mujtaba, “Deep-EmoRU: mining emotions from roman urdu text using deep learning ensemble,” Multim. Tools Appl., vol. 81, no. 30, pp. 43163-43188, 2022, doi: 10.1007/s11042-022-13147-w.
[50] X. Zhu, Y. Lou, H. Deng, and D. Ji, “Leveraging bilingual-view parallel translation for code-switched emotion detection with adversarial dual-channel encoder,” Knowl. Based Syst., vol. 235, p. 107436, 2022, doi: 10.1016/j.knosys.2021.107436.
[51] A.B. Warriner, V. Kuperman, and M. Brysbaert, “Norms of valence, arousal, and dominance for 13,915 English lemmas,” Behav. Res., vol. 45, pp. 1191-1207, 2013, doi: 10.3758/s13428-012-0314-x.
[52] M. Lewis et al., “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proc. 58th Annu. Meet. Assoc. Comput. Linguist. (ACL), Online, 2020, pp. 7871-7880, doi: 10.18653/v1/2020.acl-main.703.
[53] X. Wu, S. Lv, L. Zang, J. Han, and S. Hu, “Conditional BERT contextual augmentation,” in Computat. Sci. (ICCS), 19th Int. Conf., Faro, Portugal, 2019, pp. 84-95, doi: 10.1007/978-3-030-22747-0_7.
[54] C. Shorten, T.M. Khoshgoftaar, and B. Furht, “Text Data Augmentation for Deep Learning,” J. Big Data, vol. 8, no. 1, p. 101, 2021, doi: 10.1186/s40537-021-00492-0.
[55] S.Y. Feng et al., “A survey of data augmentation approaches for NLP,” in Find. Assoc. Comput. Linguist. (ACL-IJCNLP), Online, 2021, pp. 968-988. doi: 10.18653/v1/2021.findings-acl.84.
[56] A. Khosravi, M. Kelarestaghi, and M. Purmohammad, “Emotion detection in persian text: A machine learning model,” Biannu. J. Iranian Psychol. Assoc., vol. 14, no. 1, pp. 42-48, 2019, doi: 10.29252/bjcp.14.1.42 [In Persian].
[57] S.S. Sadeghi, H. Khotanlou, and M. Rasekh Mahand, “Automatic persian text emotion detection using cognitive linguistic and deep learning,” J. AI Data Mining, vol. 9, no. 2, pp. 169-179, 2021, doi: 10.22044/jadm.2020.9992.2136.
[58] A. Abaskohi, N. Sabri, and B. Bahrak, “Persian emotion detection using ParsBERT and imbalanced data handling approaches,” arXiv, CoRR abs/2211.08029, 2022, doi: 10.48550/arXiv.2211.08029.
[59] A. Khodaei, A. Bastanfard, H. Saboohi, and H. Aligholizadeh, “Deep emotion detection sentiment analysis of persian literary texts,” Research Square preprint, 2022, doi: 10.21203/rs.3.rs-1796157/v1.