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Total Engagement: Using Games and Virtual Worlds to Change the Way HosseinSadrfatemehmohades deilamimortezatarkhan6152021728910.66224/jict.16362.13.47.72http://jour.aicti.ir/fa/Article/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362http://jour.aicti.ir/fa/Article/Download/16362[1] N. Tsapatsoulis and C. Djouvas, "Opinion mining from social media short texts: Does collective intelligence beat deep learning?," Frontiers Robotics AI, 2019.[2] H. Sadr, M. M. Pedram, and M. Teshnehlab, "Convolutional Neural Network Equipped with Attention Mechanism and Transfer Learning for Enhancing Performance of Sentiment Analysis," Journal of AI and Data Mining, pp. -, 2021, doi: 10.22044/jadm.2021.9618.2100.[3] P. 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Personality impressions and verbal content in social video," in Proceedings of the 15th ACM on International conference on multimodal interaction, 2013, pp. 119-126. An Intelligent Model for Multidimensional Personality Recognition of Users using Deep Learning MethodsAbstract:Due to the significant growth of textual information and data generated by humans on social networks, there is a need for systems that can automatically analyze the data and extract valuable information from them. One of the most important textual data is people's opinions about a particular topic that are expressed in the form of text. Text published by users on social networks can represent their personality. Although machine learning based methods can be considered as a good choice for analyzing these data, there is also a remarkable need for deep learning based methods to overcome the complexity and dispersion of content and syntax of textual data during the training process. In this regard, the purpose of this paper is to employ deep learning based methods for personality recognition. Accordingly, the convolutional neural network is combined with the Adaboost algorithm to consider the possibility of using the contribution of various filter lengths and gasp their potential in the final classification via combining various classifiers with respective filter sizes using AdaBoost. The proposed model was conducted on Essays and YouTube datasets. Based on the empirical results, the proposed model presented superior performance compared to other existing models on both datasets.Keywords: Deep learning, Convolutional neural network, Adaboost combinational model, Personality recognition, Textual datahakimehmansourSaeedehMomtaziKamranLayeghi61520219010410.66224/jict.16363.13.47.90http://jour.aicti.ir/fa/Article/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363http://jour.aicti.ir/fa/Article/Download/16363نصراله پور، ف.، بحرانی، م.، بیجن خان، م. (1398). استفاده از روش‌های یادگیری ماشین جهت پیش‌بینی نوسانات نرخ ارز در متون خبری اقتصادی فارسی. اولین همایش ملی هوش مصنوعی و محاسبات نرم در علوم انسانی، مقاله کامل چاپ شده، Akhtar, M.S., Kumar, A., Ekbal, A. and Bhattacharyya, P., 2016, December. 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