Mining Data from the Congressional Record
This work provides a domain-specific tool for policy analysts to explore connections between legislative discourse and economic trends, but it is incremental as it applies existing data storage and analysis methods to a new dataset.
The researchers tackled the problem of analyzing the US Congressional Record as a policy tool by storing and processing data from 1789 to the present, using AWS and Solr to query tax-related language frequency and compare it to six economic indicators, with preliminary results showing potential relationships.
We propose a data storage and analysis method for using the US Congressional record as a policy analysis tool. We use Amazon Web Services and the Solr search engine to store and process Congressional record data from 1789 to the present, and then query Solr to find how frequently language related to tax increases and decreases appears. This frequency data is compared to six economic indicators. Our preliminary results indicate potential relationships between incidence of tax discussion and multiple indicators. We present our data storage and analysis procedures, as well as results from comparisons to all six indicators.