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When journalists cover a news story, they can cover the story from multipleangles or perspectives. A news article written about COVID-19 for example,might focus on personal preventative actions such as mask-wearing, whileanother might focus on COVID-19's impact on the economy. These perspectives arecalled "frames," which when used may influence public perception and opinion ofthe issue. We introduce a Web-based system for analyzing and classifying framesin text documents. Our goal is to make effective tools for automatic framediscovery and labeling based on topic modeling and deep learning widelyaccessible to researchers from a diverse array of disciplines. To this end, weprovide both state-of-the-art pre-trained frame classification models onvarious issues as well as a user-friendly pipeline for training novelclassification models on user-provided corpora. Researchers can submit theirdocuments and obtain frames of the documents. The degree of user involvement isflexible: they can run models that have been pre-trained on select issues;submit labeled documents and train a new model for frame classification; orsubmit unlabeled documents and obtain potential frames of the documents. Thecode making up our system is also open-sourced and well-documented, making thesystem transparent and expandable. The system is available on-line athttp://www.openframing.org and via our GitHub pagehttps://github.com/davidatbu/openFraming .

OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames