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
2171060319 2032158738
The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis. ABSTRACT Social scientists interested in mixed-methods research have traditionally turned to human annotators to classify the documents or events used in their analyses. The rapid growth of digitized government documents in recent years presents new opportunities for research but also new challenges. With more and more data coming online, relying on human annotators becomes prohibitively expensive for many tasks. For researchers interested in saving time and money while maintaining confidence in their results, we show how a particular supervised learning system can provide estimates of the class of each document (or event). This system maintains high classification accuracy and provides accurate estimates of document proportions, while achieving reliability levels associated with human efforts. We estimate that it lowers the costs of classifying large numbers of complex documents by 80 or more.