Abstract
Developing infrastructure and intelligent utilities for smart cities is an important trend in the world as well as in Vietnam. Thus, it is important to assist developers in building services for open data and smart city utilities. This motivates our proposal to develop a flexible platform with useful components, which can be integrated to develop these solutions quickly, to listen and analyze data from different social media sources with the diversification of data types, to provide open data providers in smart cities. Our method focuses on the ability to flexibly integrate artificial intelligence applications into the system to be able to both analyze effectively social events and serve smart cities in creating open data providers. We do not develop a particular system, but we create a platform, including different components, which are easy to be extended and integrated to create specific applications. To evaluate our platform, we develop four systems, including a face recognition system for celebrity recognition in news videos, an object detection system for brand logo recognition, a video highlighting system for summarizing football matches, and a text analysis system serving for keyword occurrences and emotional text analysis for admissions of universities. In these systems, we have collected and analyzed nearly 1000 videos from CNN, CBSN, FIFATV channels on YouTube, thousands of posts from admission pages of universities on Facebook. Each system gives a unique meaning to each specific situation for open data providers in smart cities.
T.-C. Le and Q.-V. Nguyen—Both authors contribute equally.
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This research is supported by research funding from Honors Program, University of Science, Vietnam National University - Ho Chi Minh City.
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Le, TC., Nguyen, QV., Tran, MT. (2020). Flexible Platform for Integration, Collection, and Analysis of Social Media for Open Data Providers in Smart Cities. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. FDSE 2020. Lecture Notes in Computer Science(), vol 12466. Springer, Cham. https://doi.org/10.1007/978-3-030-63924-2_18
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