I think the Guest Editor's Intro, written by a computer scientist and political scientist, is well worth the read. http://www.jitp.net/files/v005001/JITP5-1_Editors_Note.pdf The ITP section of APSA (fast growing these days) just voted to adopt JITP as the official journal of the section at the recent APSA meeting. CONTENTS Guest Editors' Introduction Text Annotation for Political Science Research Page Range: 1 - 6 DOI: 10.1080/19331680802149590 Claire Cardie, John Wilkerson Text Annotation and the Cognitive Architecture of Political Leaders: British Prime Ministers from 1945–2008 Page Range: 7 - 18 DOI: 10.1080/19331680802149624 Stephen Benedict Dyson While differences in the personalities of leaders dominate popular discussion of politics, the systematic academic study of these factors has long been beset by problems of conceptualization and measurement—difficulties that have led many in political science to conclude that such studies are not worth the effort. In this light, one of the most exciting recent developments in political psychology has been the emergence of text analysis schemes, and accompanying automation software, that offer the possibility of treating what leaders say as indicative of how they think. In this essay, I consider a text analysis protocol designed to isolate the cognitive architecture of political leaders, in particular their characteristic information processing propensities, and apply the protocol to a comprehensive set of text: the universe of prime ministerial responses to foreign policy questions in the British House of Commons from 1945–2008. The resulting data, encompassing twelve separate prime ministers, shows that the technique can discriminate reliably between individuals and exhibits promising signs of validity. Keywords: Text annotation, political psychology, foreign policy analysis, British prime ministers CORPS: A Corpus of Tagged Political Speeches for Persuasive Communication Processing Page Range: 19 - 32 DOI: 10.1080/19331680802149616 Marco Guerini, Carlo Strapparava, Oliviero Stock In political speech, even if the audience is sympathetic to the speaker and does not need to be persuaded, it tends to react or respond to signals of persuasive communication (including an expected theme, a name, an expression, and the tone of the voice). In this article, we describe the creation of a corpus of political speeches tagged with audience reactions, such as applause, as indicators of persuasive expressions. We hypothesize that corpora of this kind can be usefully employed in the qualitative analysis of political communication. In addition, we present a corpus-based approach for persuasive expression mining that relies on techniques from natural language processing (NLP). We show how the approach can support the analysis of political communication, providing insights well beyond those of traditional word-counting analysis techniques. Keywords: Persuasion, natural language processing, political communication, corpora collection Classifying Party Affiliation from Political Speech Page Range: 33 - 48 DOI: 10.1080/19331680802149608 Bei Yu, Stefan Kaufmann, Daniel Diermeier In this article, we discuss the design of party classifiers for Congressional speech data. We then examine these party classifiers' person-dependency and time-dependency. We found that party classifiers trained on 2005 House speeches can be generalized to the Senate speeches of the same year, but not vice versa. The classifiers trained on 2005 House speeches performed better on Senate speeches from recent years than on older ones, which indicates the classifiers' time-dependency. This dependency may be caused by changes in the issue agenda or the ideological composition of Congress. Keywords: Machine learning, text classification, generalizability, ideology, evaluation Recognizing Citations in Public Comments Page Range: 49 - 71 DOI: 10.1080/19331680802153683 Jaime Arguello, Jamie Callan, Stuart Shulman Notice and comment rulemaking is central to how U.S. federal agencies craft new regulation. E-rulemaking, the process of soliciting and considering public comments that are submitted electronically, poses a challenge for agencies. The large volume of comments received makes it difficult to distill and address the most substantive concerns of the public. This work attempts to alleviate this burden by applying existing machine learning techniques to the problem of recognizing citation sentences. A citation in this context is defined as a statement in which the author of the public comment references an external source of factual information that is associated with a specific person or organization. The problem is formulated as a binary classification problem: Is a specific person or organization mentioned in a sentence being referenced as an external source of information? We show that our definition of a citation is reproducible by human judges and that citations can be detected using machine learning techniques with some success. Casting this as a machine learning problem requires selecting an appropriate representation of the sentence. Several feature sets are evaluated individually and in combination. Superior results are obtained by combining feature sets. Syntactic features, which characterize the structure of the sentence rather than its content, significantly improve accuracy when combined with other features, but not when used in isolation. Although prediction error rate is adequate, coverage could be improved. An error analysis enumerates short-term and long-term challenges that must be overcome to improve recall. Keywords: Citation analysis, public comments, e-rulemaking, text mining, information extraction, machine learning Good News or Bad News? Conducting Sentiment Analysis on Dutch Text to Distinguish Between Positive and Negative Relations Page Range: 73 - 94 DOI: 10.1080/19331680802154145 Wouter van Atteveldt, Jan Kleinnijenhuis, Nel Ruigrok, Stefan Schlobach Many research questions in political communication can be answered by representing text as a network of positive or negative relations between actors and issues such as conducted by semantic network analysis. This article presents a system for automatically determining the polarity (positivity/negativity) of these relations by using techniques from sentiment analysis. We used a machine learning model trained on the manually annotated news coverage of the Dutch 2006 elections, collecting lexical, syntactic, and word-similarity based features, and using the syntactic analysis to focus on the relevant part of the sentence. The performance of the full system is significantly better than the baseline with an F1 score of .63. Additionally, we replicate four studies from an earlier analysis of these elections, attaining correlations of greater than .8 in three out of four cases. This shows that the presented system can be immediately used for a number of analyses. Keywords: Sentiment analysis, valence, polarity, political communication, automatic content analysis, semantic network analysis Automatic Annotation of Semantic Fields for Political Science Research Page Range: 95 - 120 DOI: 10.1080/19331680802149640 Beata Beigman Klebanov, Daniel Diermeier, Eyal Beigman This article discusses methods for automatic annotation of political texts for semantic fields—groups of words with related meanings. This type of annotation is useful when studying political communication, such as legislative debate or political speeches. We present three types of automatic annotation: unsupervised clustering, dictionary-based approaches, and a method based on relevant experimental data. All methods are applied to analyzing Margaret Thatcher's political rhetoric. For this data, we find that unsupervised clustering is most useful for tracing topics; dictionary-based methods are most effective in a comparative setting; whereas the last method is the most promising for detecting off-topic, singular uses of semantic domains, which are often rhetorical tools used to achieve a political end. Applicability, strengths, and weaknesses of each method and of their combinations are addressed in detail. Keywords: Political communication, speech, rhetoric, semantic fields, topic, framing, clustering, lexical cohesion, Thatcher, Blair Workbench Note An Automated Approach to Investigating the Online Media Coverage of U.S. Presidential Elections Page Range: 121 - 132 DOI: 10.1080/19331680802149582 Arno Scharl, Albert Weichselbraun This paper presents the U.S. Election 2004 Web Monitor, a public Web portal that captured trends in political media coverage before and after the 2004 U.S. presidential election. Developed by the authors of this article, the webLyzard suite of Web mining tools provided the required functionality to aggregate and analyze about a half-million documents in weekly intervals. The study paid particular attention to the editorial slant, which is defined as the quantity and tone of a Web site's coverage as influenced by its editorial position. The observable attention and attitude toward the candidates served as proxies of editorial slant. The system identified attention by determining the frequency of candidate references and measured attitude towards the candidate by looking for positive and negative expressions that co-occur with these references. Keywords and perceptual maps summarized the most important topics associated with the candidates, placing special emphasis on environmental issues. Keywords: U.S. presidential elections, media monitoring, Web mining, natural language processing, semantic orientation, keyword analysis Workbench Note Media Monitoring by Means of Speech and Language Indexing for Political Analysis Page Range: 133 - 146 DOI: 10.1080/19331680802149632 Iason Demiros, Harris Papageorgiou, Vassilios Antonopoulos, Andreas Pipis, Athena Skoulariki In this article, we describe a media monitoring system that we have developed and implemented for the Secretariat General of Communication and Secretariat General of Information in Greece (SGC-SGI). The system applies emerging technologies for audiovisual recording, speech recognition, language processing, multimedia indexing, and retrieval, all integrated into a large video and audio library that covers broadcast news and current affairs in Greek and English. It assists SGC-SGI in compiling information; annotating and analyzing news; and monitoring national, political, social, economic, cultural, and environmental issues concerning Greece in general. Keywords: Video annotation, speech recognition, multimedia information retrieval, e-government, political analysis, media content analysis Book Review The Internet and National Elections: A Comparative Study of Web Campaigning, Randolph Kluver, Nicholas Jankowski, Kristen Foot, and Steven Schneider (Eds.) New York: Routledge, 2007, 279 pages Page Range: 147 - 148 DOI: 10.1080/19331680801979062 Arthur Sanders Book Review Radical Democracy and the Internet: Interrogating Theory and Practice, Lincoln Dahlberg and Eugenia Siapera Basingstoke, UK: Palgrave, 2007, 272 pages Page Range: 149 - 150 DOI: 10.1080/19331680802132612 Stephen Coleman Book Review Zero Comments: Blogging and Critical Internet Culture, Geert Lovink New York: Routledge, 2007, 344 pages Page Range: 150 - 151 DOI: 10.1080/19331680802042282 Kevin Wallsten Book Review Cybercrime: Digital Cops in a Networked Environment, Jack M. Balkin, James Grimmelmann, Eddan Katz, Nimrod Kozlovski, Shlomit Wagman, and Tal Zarsky (Eds.) New York: New York University Press, 2006, 276, pages Page Range: 151 - 153 DOI: 10.1080/19331680802042324 David S. Wall Book Review Mobile Communication and Society: A Global Perspective, Manuel Castells, Mireia Fernández-Ardèvol, Jack Linchuan Qiu, and Araba Sey Cambridge, MA: The MIT Press, 2007, 331 pages Page Range: 154 - 155 DOI: 10.1080/19331680802042373 Kenneth Rogerson -- Dr. Stuart W. Shulman Assistant Professor Department of Political Science University of Massachusetts Amherst http://people.umass.edu/stu/ stu@polsci.umass.edu Editor, Journal of Information Technology & Politics http://www.jitp.net Director, QDAP-UMass http://people.umass.edu/stu/QDAP-UMass/ Associate Director, National Center for Digital Government http://www.umass.edu/digitalcenter/