Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7831
Title: Intellectual social scanning and analytics platform
Authors: Lau, Chi-Yat 
Dr. YUEN Man-Ching, Connie 
Cheng, Wing-Fat 
Lau, Ming-Yeung 
Chan, Wing-Fu 
Wong, Hin-Ching 
Issue Date: 2022
Source: Lau, Chi, Yuen, Man-Ching, Cheng, Wing-Fat, Lau, Ming-Yeung, Chan, Wing-Fu & Wong, Hin-Ching (2022 Dec 9-11). Intellectual social scanning and analytics platform. In BigDataSE (Ed.). 2022 IEEE 16th International Conference on Big Data Science and Engineering (BigDataSE). 2022 IEEE 16th International Conference on Big Data Science and Engineering (BigDataSE), Wuhan, China (pp. 13-20). IEEE.
Conference: 16th IEEE International Conference on Big data Science and Engineering (BigDataSE 2022) 
Abstract: Social media platforms such as Twitter have become a vital stage nowadays, and people can express their opinions on their concerning social issues and current affairs through Social Media platforms. Thus, social media platforms contain massive raw data that can be used to analyze their view on current affairs. In this paper, we propose to develop a platform to analyze people's opinions scientifically and systematically under Artificial Intelligence frameworks, and Natural Language Processing approaches. In this Analytic platform, several analytics directions are applied to this platform to discover some hidden patterns and knowledge. The platform will display several statistical outcomes and dashboards for stakeholders to understand the people's attitude toward particular social issues or affairs. The platform also classifies similar keywords and content to understand people's concerns in current social issues or matters. Furthermore, the platform also intellectually detects the potential topic and social worries in future. These outcomes provide innovative insight and honest feedback to Governments and news media when citizens suffer specific social issues or critical affairs. To illustrate the functions of the platform, we choose the 2020 US Presidential election as the use case.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/7831
ISBN: 978-1-6654-6523-6
978-1-6654-6524-3
DOI: 10.1109/BigDataSE56411.2022.00006
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