# Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees

arXiv: https://arxiv.org/abs/2109.08855 · HTML: https://mlsanity.com/paper/2109.08855/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 18 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [Detection of Self-Introductions in Legislative Testimony](https://mlsanity.com/paper/2608.07891/) (2026)

## Abstract (arXiv)

In US State government legislatures, most of the activity occurs in committees made up of lawmakers discussing bills. When analyzing, classifying or summarizing these committee proceedings, some important features become broadly interesting. In this paper, we engineer four useful features, two applying to lawmakers (engagement and absence), and two to non-lawmakers (stance and affiliation). We propose a system to automatically track the affiliation of organizations in public comments and whether the organizational representative supports or opposes the bill. The model tracking affiliation achieves an F1 of 0.872 while the support determination has an F1 of 0.979. Additionally, a metric to compute legislator engagement and absenteeism is also proposed and as proof-of-concept, a list of the most and least engaged legislators over one full California legislative session is presented.