8f154625-a6f2-4699-a235-ac650399a8ac beie director@blueeyesintelligence.org 10.35940/ijitee.J9959.13100924 10.1016/j.chb.2018.12.029Chatterjee, A., Gupta, U., Chinnakotla, M. K., Srikanth, R., Galley, M. & Agrawal, P. Understanding emotions in text using deep learning and big data. Comput. Hum. Behav. 93, 309-317 (2019). https://doi.org/10.1016/j.chb.2018.12.02910.1016/j.ipm.2022.102872Lin, S. Y., Kung, Y. C. & Leu, F. Y. Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis. Inf. Process. Manage. 59, 102872 (2022). https://doi.org/10.1016/j.ipm.2022.10287210.1016/j.eswa.2021.115819Kazmaier, J. & Vuuren, J. H. v. The power of ensemble learning in sentiment analysis. Expert Syst. Appl. 187, 115819 (2022). https://doi.org/10.1016/j.eswa.2021.115819 https://doi.org/10.1016/j.eswa.2021.11581910.1016/j.ipm.2021.102756Briskilal, J. & Subalalitha, C. N. An ensemble model for classifying idioms and literal texts using BERT and RoBERTa. Inf. Process. Manage. 59, 102756 (2022). https://doi.org/10.1016/j.ipm.2021.102756 https://doi.org/10.1016/j.ipm.2021.10275610.1016/j.ipm.2019.102185Li, C., Bao, Z., Li, L. & Zhao, Z. Exploring temporal representations by leveraging attention-based bidirectional LSTM-RNNs for multi-modal emotion recognition. Inf. Process. Manage. 57, 102185 (2020). https://doi.org/10.1016/j.ipm.2019.10218510.1109/TAFFC.2018.2807817Colnerič, N. & Demšar, J. Emotion recognition on Twitter: Comparative study and training a unison model. IEEE Trans. Affect. Comput. 11, 433-446 (2020). https://doi.org/10.1109/TAFFC.2018.280781710.1155/2022/2645381Bharti, S. K., Varadhaganapathy, S., Gupta, R. K., Shukla, P. K., Bouye, M., Hingaa, S. K. & Mahmoud, A. Text-based emotion recognition using deep learning approach. Comput. Intell. Neurosci. 2022, 2645381 (2022). https://doi.org/10.1155/2022/2645381 https://doi.org/10.1155/2022/264538110.1016/j.asoc.2019.105724Hung, J. C., Lin, K. C. & Lai, N. X. Recognizing learning emotion based on convolutional neural networks and transfer learning. Appl. Soft Comput. 84, 105724 (2019). https://doi.org/10.1016/j.asoc.2019.105724 https://doi.org/10.1016/j.asoc.2019.10572410.1016/j.ipm.2020.102435Behera, R. K., Jena, M., Rath, S. K. & Misra, S. Co-LSTM: Convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manage. 58, 102435 (2021). https://doi.org/10.1016/j.ipm.2020.10243510.1007/s11042-024-19032-yVora, S. & Mehta, R. G. HDEL: A hierarchical deep ensemble approach for text-based emotion detection. Multimed. Tools Appl. (2024). https://doi.org/10.1007/s11042-024-19032-y https://doi.org/10.1007/s11042-024-19032-y10.1016/j.procs.2023.01.141Ghosal, S. & Jain, A. Depression and suicide risk detection on social media using fasttext embedding and XGBoost classifier. Procedia Comput. Sci. 218, 1631-1639 (2023). https://doi.org/10.1016/j.procs.2023.01.14110.1109/BigComp48618.2020.00014Park, S. H., Bae, B. C. & Cheong, Y. G. Emotion recognition from text stories using an emotion embedding model. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), 2020. https://doi.org/10.1109/BigComp48618.2020.0001410.3115/v1/P14-1062Kalchbrenner, N., Grefenstette, E. & Blunsom, P. A convolutional neural network for modeling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 655-665 (2014). https://doi.org/10.3115/v1/P14-106210.3115/v1/D14-1181Kim, Y. Convolutional neural networks for sentence classification. In Proc. Conf. Empirical Methods in Natural Language Processing, 1746-1751 (2014). https://doi.org/10.3115/v1/D14-118110.1007/978-3-030-30487-4_16Duque, A. B., Santos, L. J., Macedo, D. & Zanchettin, C. Squeezed very deep convolutional neural networks for text classification. In International Conference on Artificial Neural Networks, 193-207 (Springer, 2019). https://doi.org/10.1007/978-3-030-30487-4_1610.1016/j.jbi.2021.103699Ibrahim, M. A., Khan, M. U. G., Mehmood, F., Asim, M. N. & Mahmood, W. Ghs-net: A generic hybridized shallow neural network for multi-label biomedical text classification. J. Biomed. Inform. 116, 103699 (2021). https://doi.org/10.1016/j.jbi.2021.10369910.1016/j.scs.2022.103803Yang, D. U., Kim, B., Lee, S. H., Ahn, Y. H. & Kim, H. Y. Autodefect defect text classification in residential buildings using a multi-task channel attention network. Sustain. Cities Soc. 103803 (2022). https://doi.org/10.1016/j.scs.2022.103803Glorot, X., Bordes, A. & Bengio, Y. Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 315-323 (2011).Srivastava, N. et al. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958 (2014).10.1016/B978-0-444-53859-8.00003-5Botev, Z. I., Kroese, D. P., Rubinstein, R. Y. & L'Ecuyer, P. The cross-entropy method for optimization. In Handbook of Statistics, 31, 35-59 (2013). https://doi.org/10.1016/B978-0-444-53859-8.00003-510.3115/v1/D14-1162Pennington, J., Socher, R. & Manning, C. GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543 (2014). https://doi.org/10.3115/v1/D14-1162Goodfellow, I., Bengio, Y. & Courville, A. 6.2. 2.3 softmax units for multinoulli output distributions. Deep Learning, 180 (2016).Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014).Bergstra, J., Yamins, D. & Cox, D. D. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, 115-123 (JMLR.org, 2013).10.18653/v1/S18-1001Mohammad, S., Bravo-Marquez, F., Salameh, M. & Kiritchenko, S. SemEval-2018 task 1: Affect in tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, 1-17 (2018). https://doi.org/10.18653/v1/S18-100110.1177/053901886025004001Wallbott, H. G. & Scherer, K. R. How universal and specific is emotional experience? Evidence from 27 countries on five continents. Soc. Sci. Inf. 25, 763-795 (1986). https://doi.org/10.1177/05390188602500400110.1109/TAFFC.2019.2926724CrowdFlower. Sentiment Analysis: Emotion in Text (2016). https://doi.org/10.1109/TAFFC.2019.292672410.1109/TAFFC.2019.2926724Akhtar, M. S., Ghosal, D., Ekbal, A., Bhattacharyya, P. & Kurohashi, S. All-in-One: Emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Trans. Affect. Comput. 13, 285-297 (2022). https://doi.org/10.1109/TAFFC.2019.2926724Bostan, L. A. M. & Klinger, R. An analysis of annotated corpora for emotion classification in text. In Proceedings of the 27th International Conference on Computational Linguistics, 2104-2119 (2018).10.18653/v1/D17-1169Felbo, B., Mislove, A., Søgaard, A., Rahwan, I. & Lehmann, S. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion, and sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 1615-1625 (Assoc. Comput. Linguist., 2017). https://doi.org/10.18653/v1/D17-116910.1016/j.dss.2018.09.002Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S. & Prendinger, H. Deep learning for affective computing: Text-based emotion recognition in decision support. Decis. Support Syst. 115, 24-35 (2018). https://doi.org/10.1016/j.dss.2018.09.00210.1109/ACCESS.2019.2934529Batbaatar, E., Li, M. & Ryu, K. Semantic-emotion neural network for emotion recognition from text. IEEE Access 7, 111866-111878 (2019). https://doi.org/10.1109/ACCESS.2019.293452910.3390/app13137502Rei, L. & Mladenić, D. Detecting fine-grained emotions in literature. Appl. Sci. 13, 7502 (2023). https://doi.org/10.3390/app13137502Youngquist, O. An ensemble neural network for the emotional classification of text. In the Thirty-Third International Flairs Conference (2020).10.35940/ijitee.F8804.0410621Reddy, M. V. K., & Pradeep, Dr. S. (2021). Envision Foundational of Convolution Neural Network. In International Journal of Innovative Technology and Exploring Engineering (Vol. 10, Issue 6, pp. 54-60). https://doi.org/10.35940/ijitee.f8804.041062110.35940/ijeat.B3279.129219Kumar, P., & Rawat, S. (2019). Implementing Convolutional Neural Networks for Simple Image Classification. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3616-3619). https://doi.org/10.35940/ijeat.b3279.12921910.35940/ijrte.D8326.118419Priyatharshini, Dr. R., Ram. A.S, A., Sundar, R. S., & Nirmal, G. N. (2019). Real-Time Object Recognition using Region based Convolution Neural Network and Recursive Neural Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 4, pp. 2813-2818). https://doi.org/10.35940/ijrte.d8326.118419