English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The increasing digitization of political speech has opened the door tostudying a new dimension of political behavior using text analysis. This workinvestigates the value of word-level statistical data from the US CongressionalRecord--which contains the full text of all speeches made in the USCongress--for studying the ideological positions and behavior of senators.Applying machine learning techniques, we use this data to automaticallyclassify senators according to party, obtaining accuracy in the 70-95% rangedepending on the specific method used. We also show that using text to predictDW-NOMINATE scores, a common proxy for ideology, does not improve upon thesealready-successful results. This classification deteriorates when applied totext from sessions of Congress that are four or more years removed from thetraining set, pointing to a need on the part of voters to dynamically updatethe heuristics they use to evaluate party based on political speech. Text-basedpredictions are less accurate than those based on voting behavior, supportingthe theory that roll-call votes represent greater commitment on the part ofpoliticians and are thus a more accurate reflection of their ideologicalpreferences. However, the overall success of the machine learning approachesstudied here demonstrates that political speeches are highly predictive ofpartisan affiliation. In addition to these findings, this work also introducesthe computational tools and methods relevant to the use of political speechdata.

"Read My Lips": Using Automatic Text Analysis to Classify Politicians by Party and Ideology