The document describes a pipeline developed by Kuhan Wang to analyze textual features on content URLs and their relationship to user engagement. The pipeline scrapes URLs, processes text, extracts keywords, models features, and collects user engagement data to iteratively update keywords. It attempts logistic regression classification of engagement as clicked/not clicked using bag-of-words features. Validation randomly splits data into training and test sets to generate a distribution of precision and recall scores. The pipeline is delivered to the company in Python code to implement, along with extracted top keyword rankings and project details.

1. FeatureRank
-
An Insight Data Science Consulting Project
Kuhan Wang
October 8th, 2015
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2. Consulting Scenario
Company X wishes to maximize user engagement through
optimal placement of advertisements on content URLs.
Ad Type: Tourism
Keyword: Cuba
Keyword:
Package Tour
Keyword: Airplane
Ad Type X
Keyword 1
Keyword 2
Keyword 3
Keyword N
.
.
.
Example: Tourism ads not ideal on investment content URL.
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3. A Pipeline to Analyze Textual Features
Developed and implemented a pipeline to analyze
importance of textual feature on content URLs relative to
engagement.
Scrape
URL
Process
Text
Model
Features
Extract
Keywords
Update
Keywords
Collect Data, Reiterate
Begin
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4. User Engagement Data
Occurrences
Counts
Summary of Engagement Data
Page Loaded
Ad Viewed
Ad Clicked
Summary of Engagement Data
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5. Modeling
Attempted linear regression.
Classify engagement as yes/no.
- Features are bags of words from content URL.
Word Count
0 1 2 3 4 5 6 7 8 9 10
Probability[%]
0
0.2
0.4
0.6
0.8
1
Logistic Classification Model
Ad Clicked
Ad Not Clicked
Logistic Classification Model
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6. Validation
Randomly split data into training/test sets.
- Generate distribution of validation scores.
Precision
0.55 0.6 0.65 0.7 0.75 0.8 0.85
Recall
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
NumberofMCToys
0
5
10
15
20
25
30
Distribution of Precision vs Recall
〉Precision, Recall〈
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7. Deliverables
Extracted keywords:
Rank Ad Type 1 Ad Type 2 Ad Type 3 Ad Type 4
1 debt coordinator mortgage gold
2 gift administrative home 0
3 profit minimum procurement stock
4 check minimum wage loan fund
5 balance reports trustee event
Pipeline in Python is delivered to company for
implementation.
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8. About Myself
PhD Particle Physics, McGill University, researcher on the
Large Hadron Collider.
Lead the search for microscopic black holes as part of the
ATLAS Collaboration.
About project and myself at http://kuhanw.zohosites.com/.
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9. Backup
Feature Frequency/Documents
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
RelativeNumberofDocuments[%]
4−
10
3−
10
2−
10
1−
10
1
Ad Type 1Ad Type 1
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10. 10 / 11

11. FeatureRank
Kuhan Wang1
1. Insight Data Science
October 2, 2015
Abstract
FeatureRank is a software tool for extracting correlations between text
ngram features and user engagement, thereby optimizing the placement
of financial widgets on URL articles.
1 Directory Structure
• /
processing.py
Pre-processing to parse relevant information from engagement csv files.
crawl.py
A simple web crawler that pulls the title and < p > tag text from URLs.
FeatureRank.py
Driver file to execute main functions.
feature_extraction_model.py
The core program that contains the machine learning algorithms.
post_processing.py
Post processing to produce evaluation metrics and ngram rankings. 11 / 11