Supporting Frame Analysis using Text Mining S. Ananiadou 1 , D. Weissenbacher 1 , B. Rea 1 , E. Pieri 2 , F. Vis 2 , Y. Lin 2 , R. Procter 2 , P. Halfpenny 2 1 National Centre for Text Mining, University of Manchester, 131 Princess Street, M1 7DN, UK 2 National Centre for e-Social Science, University of Manchester, Oxford Rd, Manchester M13 9PL, UK Corresponding author: Sophia.Ananiadou@manchester.ac.uk Abstract. In this paper, we describe how text mining (TM) has been used to support frame analysis within the context of the ASSIST project. This technology is capable of retrieving knowledge from unstructured text and presenting it to researchers in a concise form. In the course of the ASSIST project, which is a collaboration between social researchers and text mining experts, we have designed and prototyped a specialized search engine to help researchers who specialise in the frame analysis of news articles After describing each of the TM modules which compose our system and the functionalities they provide, we present the results of their evaluation. Introduction The rapid increase of digital content in newswires and other sources allows information to be made immediately available to a wide audience. Social scientists, faced with an overwhelming amount of information, are turning to new automated techniques to support their research methodology and to handle the huge amount of information. Text mining (TM) is a novel technology which retrieves knowledge from unstructured text and presents the distilled knowledge to users in a concise form (Ananiadou et al., 2009). The advantage of text mining is that it enables researchers to collect, maintain, interpret, curate and discover knowledge needed for research or education in an efficient and systematic matter (Ananiadou and McNaught, 2006). Text mining annotates documents with entities and facts of interest to the user, thus enabling information extraction (IE). Its goal is to extract important information from textual sources without requiring the end user of the information to read the text themselves (McNaught and Black, 2006). In this paper, we describe the ASSIST project 1 in which text mining has been used to support 1 http://www.nactem.ac.uk/assist/