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Tweede Kamer Parliamentary Embedding Analysis

Goal

Track how MPs shift politically over time and map motions onto a meaningful ideological axis, by embedding both MPs and motions into a shared vector space.

Data

Source	Content
MP × motion vote matrix	yes / no / abstain per MP per motion
Motion text	Dutch-language motion descriptions
MP metadata	name, party, entry/exit dates
Timestamps	date of each vote

Approach: Late Fusion

Two independent embedding signals, combined per motion.

1. Vote embeddings (SVD)

Build a sparse MP × motion matrix per time window
Apply SVD to get latent vectors for both MPs and motions
Encodes political alignment from actual voting behavior

2. Text embeddings (Qwen3-0.6B)

Embed each motion's text using Qwen3-0.6B (multilingual, Dutch supported)
Encodes semantic/policy topic of the motion
Use a task instruction in English, e.g. "Retrieve semantically similar Dutch parliamentary motions"

3. Fusion

Concatenate (or weighted sum) the SVD motion vector and text vector into a single motion embedding. MPs retain their SVD vectors only.

Temporal Tracking

Time windows

Default: quarterly (flexible — can be per half-year or per N votes)
Adaptive option: fixed number of votes per window (e.g. 200) for stable SVD regardless of parliamentary rhythm

Procrustes alignment

SVD axes are arbitrary per window and cannot be compared directly. Procrustes alignment finds the optimal rotation mapping one window's space onto the previous, using overlapping MPs as anchors.

R = argmin || W1[common] - W2[common] @ R ||
W2_aligned = W2 @ R  # applied to all MPs, including newcomers

Only overlapping MPs are needed to estimate R
New MPs are placed into the aligned space via their voting pattern
High Procrustes disparity score = structural political shift, not just individual drift

Election transitions

At term boundaries (~60% MP overlap), alignment is noisier. Mitigation: chain alignments via the last quarter of the old term and first quarter of the new term, using only returning MPs.

Analysis

Question	Method
MP drift over time	trajectory of MP vector across aligned windows
Political axis	first SVD component, or defined by anchor parties (e.g. VVD vs SP)
Swing voters	MPs closest to the boundary between party clusters
Thematic clustering	UMAP on fused motion embeddings
Cross-party coalitions	motions where party cluster boundaries blur
Party cohesion	variance of MP vectors within a party per window

Stack

Component	Tool
Matrix factorization

````|

|Procrustes alignment|
````scipy.spatial.procrustes
````|

|Text embeddings|Qwen3-0.6B via 
````sentence-transformers

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