  When journalists cover a news story, they can cover the story from multiple
angles or perspectives. A news article written about COVID-19 for example,
might focus on personal preventative actions such as mask-wearing, while
another might focus on COVID-19's impact on the economy. These perspectives are
called "frames," which when used may influence public perception and opinion of
the issue. We introduce a Web-based system for analyzing and classifying frames
in text documents. Our goal is to make effective tools for automatic frame
discovery and labeling based on topic modeling and deep learning widely
accessible to researchers from a diverse array of disciplines. To this end, we
provide both state-of-the-art pre-trained frame classification models on
various issues as well as a user-friendly pipeline for training novel
classification models on user-provided corpora. Researchers can submit their
documents and obtain frames of the documents. The degree of user involvement is
flexible: they can run models that have been pre-trained on select issues;
submit labeled documents and train a new model for frame classification; or
submit unlabeled documents and obtain potential frames of the documents. The
code making up our system is also open-sourced and well-documented, making the
system transparent and expandable. The system is available on-line at
http://www.openframing.org and via our GitHub page
https://github.com/davidatbu/openFraming .
