Profile-Based Authorship Analysis To Appear in Literary and Linguistic Computing (now Digital Scholarship in the Humanities) Jonathan Dunn*, Shlomo Argamon, Amin Rasooli, Geet Kumar Illinois Institute of Technology Abstract This article presents a profile-based authorship analysis method which first categorizes texts according to social and conceptual characteristics of their author (e.g., Sex and Political Ideology) and then combines these profiles for two authorship analysis tasks: (1) determining shared authorship of pairs of texts without a set of candidate authors and (2) clustering texts according to characteristics of their authors in order to provide an analysis of the types of individuals represented in the dataset. The first task outperforms Burrowsǯ Delta by a wide margin on short texts and a small margin on long texts. The second task has no such benchmark with existing methods. The dataset for evaluating the method consists of speeches from the U.S. House and Senate from 1995 to 2013. This dataset contains both a large number of texts (42,000 in the test sets) and a large number of speakers (over 800). The article shows that this approach to authorship analysis is more accurate than existing approaches given a dataset with hundreds of authors. Further, this profile-based method makes new types of analysis possible by looking at types of individuals as well as at specific individuals. Keywords: authorship analysis, author profiles, text as data, ensemble approaches to authorship Abstract: 190 words; Article: 10,580 words * Corresponding Author