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JGAAP (Java Graphical Authorship Attribution Program) is a Java-based, modular, program for textual analysis, text categorization, and authorship attribution i.e. stylometry / textometry. JGAAP is intended to tackle two different problems, firstly to allow people unfamiliar with machine learning and quantitative analysis the ability to use cutting edge techniques on their text based stylometry / textometry problems, and secondly to act as a framework for testing and comparing the effectiveness of different analytic techniques' performance on text analysis quickly and easily.

JGAAP is developed by the Evaluating Variation in Language Laboratory (EVL Lab) and released under the AGPLv3. We strive to keep this wiki and our website, evllabs.com, up-to-date with the most recent developments of the project and our research, if you have any trouble using JGAAP, would like to make a feature request, or want to contribute please contact JGAAP Support.

News

May 23, 2013 - We had an issue when updating a server and have lost a large portion of the wiki. We will be working on rebuilding its content over the next few weeks. Thank you for your patience.

Downloads

• Stable (6.0): src, jar
• Nightly (7.0 alpha): src
• Release History

Documentation

• FAQ
• JGAAP
• New Developers
• Using JGAAP
• Javadocs

Repository Links

• GitHub Repository
• GitHub Issue Tracker

Team

Staff

• Patrick Juola
• John Noecker
• Michael Ryan
• Patrick Brennan

Graduate Students

• Erin Purnell
• Belinda Hasanaj

Undergraduate Students

• Elizabeth DeCarlo

Funding

Funding for this project has been provided by the National Science Foundation, originally through award #OCI-0721667, and recently renewed via award #OCI-1032683.
(As always, the errors ours, the credit theirs.)

Commercial Applications

• Juola & Associates

References

• Patrick Juola, John Sofko, and Patrick Brennan. (2006). "A Prototype for Authorship Attribution Studies" Literary and Linguistic Computing 21:169-178
• Patrick Juola and Harald Baayen. (2005). "A Controlled-Corpus Experiment in Authorship Identification by Cross-Entropy." Literary and Linguistic Computing , 20(Suppl 1) 59-67.
• Ad-hoc Authorship Attribution Competition Main Page
• Patrick Juola and John Sofko. (2004). "Proving and Improving Authorship Attribution." Proceedings of CaSTA-04 "The Face of Text."