🧾 What Was Analyzed
Trained neural word-embedding models were applied to large-scale parliamentary corpora from Britain, Canada, and the United States. The embeddings used are the coefficients from neural-network models that predict word use in context, augmented with political metadata to link language directly to party affiliation.
🔧 How the Models Work
- Word embeddings are treated as model coefficients that capture how words are used in context.
- Indicator variables for members’ party affiliation are included in the prediction models; these party indicators are estimated as distinct vectors referred to as "party embeddings."
- The framework produces continuous estimates that can be used to scale ideological placement and to derive other quantities of substantive interest in political research.
đź“Š How the Approach Was Evaluated
Validation compares party-embedding estimates against established measures:
- Comparative Manifestos Project indicators
- Expert survey ratings
- Roll-call vote–based measures
This multi-pronged validation assesses whether party embeddings track known dimensions of political behavior and positioning.
âś… Key Findings
- Party embeddings successfully capture latent concepts such as ideology as expressed in parliamentary language.
- Party-embedding scales align meaningfully with manifesto indicators, expert judgments, and roll-call measures, supporting their validity for ideological placement.
- The approach provides an integrated framework for studying political language that links textual patterns to party-level political attributes.
⚖️ Why It Matters
This methodology expands tools for analyzing political texts by combining neural-word representations with political metadata. It offers researchers a scalable, text-based way to estimate party positions and latent political concepts directly from parliamentary speech, complementing traditional sources like manifestos and voting records.
