1. Topic-Factorized Ideal Point Estimation Model for Legislative Voting Network Yupeng Gu †, Yizhou Sun †, Ning Jiang ‡, Bingyu Wang †, Ting Chen † † Northeastern University ‡ University of Illinois at Urbana-Champaign

2. /27 Background Federal Legislation (bill) Law The House Senate Ronald Paul Bill 1Bill 2 …… Barack Obama Ronald Paul liberalconservative Politician RepublicanDemocrat Barack Obama 2 United States Congress The HouseSenate

3. /27 Outline Background and Problem Definition Topic Factorized Ideal Point Model and Learning Algorithm Experimental Results Conclusion 3

4. /27 Legislative Voting Network 4

5. /27 Problem Definition Input: Legislative Network 5

6. /27 Existing Work 1 dimensional ideal point model (Poole and Rosenthal, 1985; Gerrish and Blei, 2011) High-dimensional ideal point model (Poole and Rosenthal, 1997) Issue-adjusted ideal point model (Gerrish and Blei, 2012) 6

7. /27 Motivations 7 Topic 1 Topic 2 Topic 3 Topic 4 Voters have different attitudes on different topics. Traditional matrix factorization method cannot give the meanings for each dimension. Topics of bills can influence politician’s voting, and the voting behavior can better interpret the topics of bills as well. Topic Model: Health Public Transport … Voting-guided Topic Model: Health Service Health Expenses Public Transport …

8. /27 Outline Background and Problem Definition Topic Factorized Ideal Point Model and Learning Algorithm Experimental Results Conclusion 8

9. /27 Topic Factorized IPM 9 Politicians Bills Terms Heterogeneous Voting Network

10. /27 Text Part 10 Politicians Bills Terms

11. /27 Text Part We model the probability of each word in each document as a mixture of multinomial distributions, as in PLSA (Hofmann, 1999) and LDA (Blei et al., 2003) BillTopicWord 11

12. /27 Voting Part 12 Politicians Bills Terms

13. /27 Voting Part YEA Topic 1 Topic 2 …… 0111111 00 111 1 1111 100 …… NAY 13 ……

14. /27 Combining Two Parts Together 14

15. /27 Learning Algorithm 15

16. /27 Learning Algorithm 16

17. /27 Outline Background and Problem Definition Topic Factorized Ideal Point Model and Learning Algorithm Experimental Results Conclusion 17

18. /27 Data Description 18

19. /27 Evaluation Measures 19

20. /27 Experimental Results Training Data setTesting Data set 20

21. /27 Parameter Study 21

22. /27 Foreign Education Individual Property Military Financial Institution Law Health Service Health Expenses Funds Public Transportation Ronald PaulBarack ObamaJoe Lieberman Case Studies Ideal points for three famous politicians: (Republican, Democrat) Ronald Paul (R), Barack Obama (D), Joe Lieberman (D) 22

23. /27 Case Studies Pick three topics:(Republican, Democrat) 23

24. /27 Case Studies Bill: H_R_4548 (in 2004) Nay R. Paul H_R_4548 24 Topic Model TF-IPM Experts

25. /27 Outline Background and Problem Definition Topic Factorized Ideal Point Model and Learning Algorithm Experimental Results Conclusion 25

26. /27 Conclusion We estimate the ideal points of legislators and bills on multiple dimensions instead of global ones. The generation of topics are guided by two types of links in the heterogeneous network. We present a unified model that combines voting behavior and topic modeling, and propose an iterative learning algorithm to learn the parameters. The experimental results on real-world roll call data show our advantage over the state-of-the-art methods. 26

27. /27 Thanks Q & A 27