Towards Deep Learning Prospects: Insights for Social Media Analytics

IEEE Access

Ali Daud¹  Rabeeh Ayaz Abbasi²  Hussain Dawood³  Abdulrahman A. Alshdadi⁴  Ameen Banjar⁴  Malik Khizar Hayat⁵  Yukun Bao⁶ 

[1] Department of Computer Science and Software Engineering, International Islamic University at Islamabad, Islamabad, Pakistan;Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan;Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia;Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia;Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan;School of Management, Huazhong University of Science and Technology, Wuhan, China;

关键词: Social media data;    dynamic network;    deep learning;    feature learning;   

DOI  :  10.1109/ACCESS.2019.2905101

来源: DOAJ

【 摘 要 】

Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. In order to the exponential development and widespread availability of digital social media (SM), analyzing these data using traditional tools and technologies is tough or even intractable. DL is found as an appropriate solution to this problem. In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented summary, following the major efforts made toward the SM analytics (SMA). Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. To this end, we also highlight the DL research challenges (such as scalability, heterogeneity, and multimodality) and future trends.

【 授权许可】

Unknown