Insights into social phenomenon can be gleaned from trends and patterns in corpora of documents associated with that phenomenon. Recent years have witnessed the use of computational techniques, mostly based on keywords, to analyze large corpora for these purposes. In this paper, we extend these techniques to incorporate semantic features. We introduce Doris, an interactive exploration tool that combines semantic features with information retrieval techniques to enable exploration of document corpora corresponding to the social phenomenon. We discuss the semantic techniques and describe an implementation on a corpus of United States (US) presidential speeches. We illustrate, with examples, how the ability to combine syntactic and semantic features in a visualization helps researchers more easily gain insights into the underlying phenomenon.
In @cite_5 , Grimmer uses a statistical topic model on press releases from the House of Representatives from 2005 to 2010 to demonstrate the shift in portrayed representation due to electoral pressure. The author shows how members of the House change rhetoric, specifically in terms of taking credit, due to political pressure. Hillard, Purpura and Wilkerson @cite_8 examine over 300,000 congressional bill titles (that researchers have assigned topics to) and use supervised learning algorithms to allocate topics. The authors show a successful method of classifying large sets of data computationally. Hopkins and King @cite_1 develop a method that gives estimates of category proportions for large sets of data. Using data sets that include relevant political opinions, the authors focus on document category proportions rather than absolute counts of individual categories.
@cite_5 @cite_1 @cite_8
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Political scientists lack methods to efficiently measure the priorities political actors emphasize in statements. To address this limitation, I introduce a statistical model that attends to the structure of political rhetoric when measuring expressed priorities: statements are naturally organized by author. The expressed agenda model exploits this structure to simultaneously estimate the topics in the texts, as well as the attention political actors allocate to the estimated topics. I apply the method to a collection of over 24,000 press releases from senators from 2007, which I demonstrate is an ideal medium to measure how senators explain their work in Washington to constituents. A set of examples validates the estimated priorities and demonstrates their usefulness for testing theories of how members of Congress communicate with constituents. The statistical model and its extensions will be made available in a forthcoming free software package for the R computing language. We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probability distribution over words for that topic. The words in a multi-author paper are assumed to be the result of a mixture of each authors' topic mixture. The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm. We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library, and learn a model with 300 topics. We discuss in detail the interpretation of the results discovered by the system including specific topic and author models, ranking of authors by topic and topics by author, significant trends in the computer science literature between 1990 and 2002, parsing of abstracts by topics and authors and detection of unusual papers by specific authors. An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer. We develop several predictive models linking legislative sentiment to legislative text. Our models, which draw on ideas from ideal point estimation and topic models, predict voting patterns based on the contents of bills and infer the political leanings of legislators. With supervised topics, we provide an exploratory window into how the language of the law is correlated with political support. We also derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we predict specific voting patterns with high accuracy. We use bill cosponsorship and roll-call vote data to compare legislators’ revealed preferences in the U.S. House of Representatives and the Argentine Chamber of Deputies. We estimate ideal points from bill cosponsorship data using principal-component analysis on an agreement matrix that included information on all bills introduced in the U.S. House (1973–2000) and Argentine Chamber (1983–2002). The ideal-point estimates of legislators’ revealed preferences based on cosponsorship data strongly correlate with similar estimates derived from roll-call vote data. Also, cosponsorship activity in the U.S. House has lower dimensionality than cosponsorship has in the Argentine Chamber. We explain this lower discrimination as a function of individual- and district-level factors in both countries. The comparative analysis of legislative voting behavior has enjoyed a resurgence of interest in the last decade (Carey 2006; Morgenstern 2004; Sieberer 2006). New statistical techniques and the greater availability of data now allow researchers to map legislative coalitions, explore party discipline, and explain political realignments in multiparty systems (see, for examples, Aleman and Saiegh 2007; Amorim Neto, Cox, and McCubbins 2003; Clinton, Jackman, and Rivers 2004; Desposato 2005; Haspel, Remington, and Smith 1998; Hix, Noury, and Roland 2006; Hug and Schulz 2007; Jones and Hwang 2005a; Londregan 2000; Morgenstern 2004; Poole 2005; and Rosenthal and Voeten 2004). Efforts to understand voting behavior in legislatures across Europe and Latin America not only expand our knowledge about lawmaking and legislative parties, but also promise to shed new light on the forces that shape legislators’ preferences within different institutional contexts.