Discover the range of analytical tools we provide for social media
SOCIAL MEDIA INTELLIGENCE AND LEARNING ENVIRONMENT (SMILE)
One platform for social media data ingestion, analysis, and data sharing. SMILE collects real time and historical
social media data from Twitter and Reddit, performs sentiment analysis, phrase mining,
entity recognition, machine learning classification, network analysis and so on. You can share your
computation result to Clowder and sharing with your collaborators.
At our playground, we provide a temporary space for your experimentation, where no data is stored beyond a day.
Discover seamless data sharing and collaboration with SMILE's social media analytical tools. Our
platform integrates with Clowder (SMM Clowder), a customizable data management framework used by
renowned science gateways. With Clowder, you
can easily share analysis outputs, visualize data, and manage access.
SMILE's user-friendly wizard allows you to effortlessly upload targeted analytics outputs to Clowder. You can also leverage Clowder's information extractor functionality for drawing quick insight of your data uploaded.
BRAND ANALYTICS ENVIRONMENT (BAE)
BAE helps practitioners to gain insight into how individuals and groups may interact with brands and various organizations. BAE investigates the relationships between users of Twitter’s machine-learned personalities and a consumer brand’s machine-learned personalities. You may find an organization’s perfect “bae” through this tool.Coming Soon...
Effortless On-Premises Deployment
Experience hassle-free deployment of our full stack of apps right on your own machine with ease.
For deployment, SMILE leverages
Helm charts to define,
install, and manage applications seamlessly. This integration simplifies the deployment and
administration of complex applications through templating and version control. Each component of SMILE,
such as the SMILE server,
graphql data server, algorithms, and MinIO instance, is encapsulated as
Docker containers and
individually deployed within Kubernetes. This approach enables independent management of component
activation, scaling, and storage configuration without causing disruptions. Furthermore, the internal
networking system provided by Kubernetes enhances the overall security of the system.
For lightweight deployments where scalability is not a concern, SMILE also offers a Docker Compose setup option. This alternative provides a simpler learning curve and requires fewer resources compared to Kubernetes.
Embracing Open Source
Our project, built upon open-sourced libraries and algorithms, is fully open-sourced and appreciates any kind of community contribution. Visit repositories for SMILE and SMILE Data Server and Analytics Algorithms on GitHub. You are welcome to fork our repositories, open issues and create pull requests to any of the public repository.
Cite our work below. Your citation will help us continue to provide and improve this platform:
Wang, C., Marini, L., Chin, C. L., Vance, N., Donelson, C., Meunier, P., & Yun, J. T.(2019, September). Social Media Intelligence and Learning Environment: an Open Source Framework for Social Media Data Collection, Analysis and Curation. In 2019 15th International Conference on eScience (eScience) (pp. 252-261). IEEE.
Yun, J. T., Vance, N., Wang, C., Marini, L., Troy, J., Donelson, C., Chin, C. L., Henderson, M. D. (2019).The Social Media Macroscope: A science gateway for research using social media data. Future Generation Computer Systems. doi:10.1016/j.future.2019.10.029