![Relevant Citations
? Gensim: Topic Modeling for Humans by Radium Hurek, open
source python package
? Wallach, H. M. (n.d.). Topic Modeling: Beyond Bag-of-Words.
Retrieved from poster link
? Gerrish, S. M., & Blei, D. M. (2011). Predicting legislative roll
calls from text. In Proc. of ICML. Retrieved from article link
? Rong, X. (2016, June 5). Word2vec Parameter Learning
Explained. doi:arXiv:1411.2738 [cs.CL]: article link
? Unsplash for stock photo](https://image.slidesharecdn.com/predictcabillwithtopicmodelsonly-paulinechow-161102081751/85/Data-Science-Predict-Bill-Passage-with-Topics-Only-18-320.jpg)
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Data Science: Predict Bill Passage with Topics Only
- 1. Data Science: Predict Success of Legislation with Topics Only Natural Language Processing with Sunlight Foundation Open States API ! Pauline Chow Fall 2016
- 2. What policies and laws relate to your well being? When I ?rst asked this question I was working and interested in transportation policy, especially walking and bicycling
- 3. ? Increase transparency of the various levels of decision making - federal, state, and local ? Effectively understand trends in public policy, in order to educate and in?uence ? Distill legislative process into logical system of features ? Extract and identify relationships of decision makers with communities, topics, and laws Why Does Analyzing Elements of Successful Legislation Important?
- 4. Data Science Steps for Predicting Legislative Results 1. Collect Data from Sunlight Foundation API and other open data sources 2. Clean text from legislative bills via web scraping, including removing html, stop words, target variable (i.e. bill passage) 3. Extract features from text in python 4. Build topics from text using Latent Dirchlet Allocation (LDA), probabilistic approach 5. Implement supervised learning models 6. Analyze results
- 5. 1. Collect: As the initial step to building predictive models, insights re?ect features from California bills text between 2009-2014
- 7. 3. How to Extract Features from Text? Sick of Having to go 2 di?erent hut buy pizza sunglass 1 1 1 2 1 1 1 1 1 1 1
- 8. 4. Build: What is the Latent Dirchlet Allocation (LDA) Topic Model? ? Finds hidden semantic structure, aka context, where topics are cluster of similar words: P(word | context) ? Each document is a mixture of topics, words and phrases, which are split into probabilities ? Tune parameters: # of words in each topic, mixture within each topic, threshold for frequency and probability ? For example: Topic A (1,2,5): breakfast 30%, pizza 10%, smoothie 5% 1 Sick of having to go to two huts for pizza and sunglasses 2 I ate a cold pizza and spinach smoothie for breakfast. 3 I wear my sunglasses at night so I can see 4 Sometimes I get really sick when I go on roller coasters 5 Co?ee only for breakfast because co?ee is for closers
- 9. 4. Build Word2Vec: Find Similarities ? Extract relationships in unstructured text ? Leverage context of documents and LDA’s probabilistic models ? Hierarchical structure of probabilities ? Derive meaning from cleaned vector of words and phrases
- 10. 5. Implement Logistic Regression: CA Bills Over Time 1. Model predicts failure better than successful legislation 2. Model with 50 versus 100 topics predictive results did not differ signi?cantly 3. Precision (TP / TP + FP) 4. Recall (TP / TP + FN) Model Classi?cation Report: All Topics Over Time target precision recall f1- score support failed 0.70 0.98 0.82 3118 passed 0.47 0.04 0.07 1360 avg / total 0.63 0.69 0.59 4478
- 11. 6. Analyze California Bills (100 Topic Models) ? Bills have an average of 6.57 number of topics, ranging from 2 - 16. ? Passage rate by topic ranged from 18% to 36%, averaging 28% for all bills in the database ? Most frequent topics of legislation relate to local government funding/taxes/leadership initiatives, health care, education, budget and taxes, and court system ? Highest and lowest passage rate topics are reviewed in the next few slides
- 12. 6. Analyze: Distribution of Topics in California Bills Top 10 Topics by Frequency Topic #s Frequency topic 48 13569 topic 11 11838 topic 51 8024 topic 73 4913 topic 6 3675 topic 63 2782 topic 1 2615 topic 22 1879 topic 64 1726 topic 45 1663
- 13. 6. What Topics Support Bill Passage? Rank Topic # Odds Ratio LDA Topics 1 70 453.981 0.023*tank + 0.019*underground + 0.015*transferor + 0.011*lie + 0.010*decennial + 0.008*storage + 0.008*cotenant + 0.008*stanford + 0.006*petroleum + 0.006*orphan 2 74 32.797 0.020*contribution + 0.014*calendar + 0.010*canyon + 0.009*lincoln + 0.009*shoulder + 0.007*stenographer + 0.006*in?ation + 0.005*dispatcher + 0.005*vine + 0.005*boyer 3 47 32.695 0.024*cemetery + 0.011*mexican + 0.010*interment + 0.007*salton + 0.006*elsinore + 0.006*tuberculosis + 0.005*burial + 0.004*bacteria + 0.004*creek + 0.004*coliform 4 42 28.312 0.008*hoover + 0.003*tricare + 0.002*shower + 0.002*crutch + 0.002*contractholders + 0.002*bath + 0.001*dme + 0.001*durable + 0.000*hcpcs + 0.000*wheelchair 5 21 25.041 0.021*wyland + 0.015*reorganization + 0.014*brown + 0.012*gordon + 0.010*presidential + 0.008*ford + 0.008*gerald + 0.008*battalion + 0.007*mitochondrial + 0.007*remembrance 6 71 9.608 0.064*andwhereas + 0.024*awareness + 0.020*week + 0.014*whereas + 0.013*violence + 0.012*woman + 0.010*disease + 0.010*resolution + 0.010*month + 0.009*furtherresolved 7 65 8.798 0.029*pipeline + 0.027*ronald + 0.022*sea + 0.013*coastal + 0.012*marine + 0.009*rise + 0.008*reagan + 0.008*thomas + 0.007*climate + 0.007*arctic 8 34 8.183 0.030*candidate + 0.011*teen + 0.011*precinct + 0.011*nomination + 0.011*poll + 0.009*freeway + 0.009*say + 0.009*dating + 0.008*teenager + 0.007*sca 9 58 5.086 0.022*autism + 0.014*nursing + 0.014*therapist + 0.013*mr + 0.013*calderon + 0.011*backpack + 0.009*credentialing + 0.008*therapy + 0.008*acupuncture + 0.008*marriage 10 44 4.899 0.021*scientist + 0.017*negrete + 0.016*fact?nding + 0.015*hepatitis + 0.015*mcleod + 0.011*maternity + 0.009*interdistrict + 0.009*knuckle + 0.008*liver + 0.005*infected 15 94 2.103 0.023*bicycle + 0.017*bus + 0.013*midwife + 0.011*deployed + 0.010*roadway + 0.009*smog + 0.008*schoolbus + 0.007*safer + 0.005*polluter + 0.005*overtaking
- 14. Sample CA Bills Containing “Strong” Topics Bill Status Bill Session, ID (Link) Topic # All Topics Passed 2011-2012-0 AB291 Underground storage tanks: petroleum: charges. 70 11, 45, 48, 70 Passed 2013-2014-0 AB1286 Personal income tax: voluntary contributions: California Breast Cancer Research Fund 74 11, 45, 74 Passed 2009-2010-0 AB1969 Elsinore Valley Cemetery 47 1, 11, 47, 48 Passed 2011-2012-0 AB2488 Vehicles: buses: length limitations 94 1, 11, 48, 49, 73, 94
- 15. 6. What Topics Have Weak Bill Passage? Rank Topic # Odds Ratio Topics -10 79 0.000498 0.057*emission + 0.050*greenhouse + 0.041*gas + 0.019*warming + 0.017*global + 0.016*climate + 0.014*air + 0.013*carbon + 0.013*reduction + 0.013*solution -9 92 0.000156 0.038*tra?cking + 0.012*duress + 0.010*menace + 0.010*?duciary + 0.010*ammunition + 0.009*chvez + 0.009*human + 0.008*achadjian + 0.008*wilk + 0.006*bigelow -8 76 0.000150 0.071*inmate + 0.065*parole + 0.026*parolee + 0.023*prison + 0.021*correction + 0.019*rehabilitation + 0.010*released + 0.009*recidivism + 0.008*reentry + 0.007*journalist -7 28 0.000113 0.036*bag + 0.024*plastic + 0.015*carryout + 0.011*positioning + 0.007*tends + 0.007*electorate + 0.006*store + 0.006*deliberately + 0.006*undetermined + 0.005*el -6 50 0.000063 0.010*romero + 0.009*antipsychotic + 0.008*medication + 0.006*dementia + 0.006*detachable + 0.005*salvage + 0.004*dietary + 0.004*psychotropic + 0.004*repurchase + 0.003*diminishes -5 24 0.000053 0.023*baby + 0.006*depression + 0.005*paratransit + 0.005*stewardship + 0.004*producer + 0.004*perinatal + 0.003*unwanted + 0.003*obstetrics + 0.003*sleep + 0.002*calhome -4 8 0.000035 0.054*gang + 0.013*immunity + 0.010*tort + 0.008*ri?e + 0.007*european + 0.007*magazine + 0.007*pervasive + 0.005*deadly + 0.005*mentally + 0.005*disordered -3 81 0.000013 0.014*interpreter + 0.007*excellence + 0.006*digitized + 0.005*reelected + 0.005*easy + 0.004*?uency + 0.004*biodegradable + 0.002*willfulness + 0.002*annoyance + 0.002*disincentive -2 10 0.000004 0.022*budget + 0.013*muratsuchi + 0.013*sawyer + 0.013*mullin + 0.012*bloom + 0.012*nazarian + 0.012*daly + 0.012*campos + 0.011*rodriguez + 0.010*dababneh -1 62 0.000001 0.005*consummated + 0.004*nonconsenting + 0.003*nonsupervisory + 0.001*peculiar + 0.001*culminating + 0.000*overdue + 0.000*reputation + 0.000*unimpeded + 0.000*foster + 0.000*licentious
- 16. Sample CA Bills Containing “Weak” Topics Bill Status Bill Session, ID (Link) Topic # All Topics Passed 2011-2012-0 SB1219 Recycling Plastic Bags 28 28, 45, 48, 57 Passed 2013-2014-0 AB1405 Subversive Organization Registration Law: repeal 92 6, 11, 20, 37, 48, 51, 66 Passed 2011-2012-0 AB220 Interstate Compact for Juveniles. 76 48, 51, 63, 76 Passed 2009-2010-0 AB863 Public utilities: municipal districts: civil service exemptions. 62 11, 30, 48, 51, 62
- 17. Next Steps for Legislative Predictions ? Add time context for bills in terms of legislative session, chamber, and major political events ? Adding features about the bill, sponsors, districts, political context, duration, committees, public comments ? Include exploratory data analysis from bill and legislator data ? Tune model to apply predictions to current bills
- 18. Relevant Citations ? Gensim: Topic Modeling for Humans by Radium Hurek, open source python package ? Wallach, H. M. (n.d.). Topic Modeling: Beyond Bag-of-Words. Retrieved from poster link ? Gerrish, S. M., & Blei, D. M. (2011). Predicting legislative roll calls from text. In Proc. of ICML. Retrieved from article link ? Rong, X. (2016, June 5). Word2vec Parameter Learning Explained. doi:arXiv:1411.2738 [cs.CL]: article link ? Unsplash for stock photo