The document discusses an algorithm developed to predict the number of H-1B work visa applications based on economic indicators. The algorithm uses data on the S&P 500, unemployment rate, sentiment analysis of news articles, and Google search trends on negative economic terms. It fits time shifts to these economic features before inputting them into a gradient boosted regression trees model to make predictions about H-1B visa applications over time based on the state of the economy.

1. H-1B Ready
Anticipating foreign visa demand!
How many H-1B work visa applications should we expect
based on how the economy has been doing?
Anne Bauer

2. Algorithm
Economic Features:
• S&P 500
• (Un)employment Rate
• Sentiment of 17,000 NYTimes news
articles on the US Economy
• Google Trends of negative economic terms
Algorithm:
• Fit a time shift to each economic
feature
• Input time-shifted features to
gradient boosted regression trees
H-1B Data:
• Public data from the
Department of Labor
• Changing formats &
conventions
• User input → mistakes!

3. AlgorithmValidation
EngineersAll Job Sectors

4. About me

5. Thank you!

6. Algorithm
S&P 500
News
Sentiment
Google
Trends
Unemployment
Peak Month?
Steady
Increase
Time Shift
Time Shift
Time Shift
Time Shift
Gradient
Boosted
Regression
Trees
Time
Series
Model

7. Algorithm

8. Job Sector Segmentation
FinanceHealth

9. Sentiment Analysis
• 17 000 NYTimes news articles on the US Economy
• Seed word lists manually curated from the most common
vocabulary in the articles
• Sentiment score built from the path similarity between the
seed words and the article words