data mining & engineering

Copyright ©2014 Visible Technologies, Inc. All rights reserved.1

Data Mining and Engineering

Lucas ParkerSenior Software Development Engineer, Research & Development

Presented by


About Visible

Our Mission: Customer ValueGlobal Authoritative Content• Most comprehensive global content sourcing model• Clean & accurate data

Powerful Search & Discovery• Only the most pertinent results, based on your criteria• Pivot and drill to identify discussion drivers

Engagement/Social CRM• Social Media workflow and engagement for individual or team• Integrate with CRM for continuous relationship management

Sophisticated Social Analytics•Measure, compare, and contrast program & communication results• Segment results by product attributes, reputation drivers, etc.

Actionable Insights•Discovery & analytics to uncover insights in real time•Holistic consumer insights, integrated with other market research


What We Do

• Domain is “social media”• Twitter, Facebook, forums, blogs, etc

• Huge data sets, lots of noise.

• Enrichment, aggregation, reporting.


Visible – Target Business Groups

• Customer Servicing• Interactions between business and customer.

• Marketing• Brand effort, campaigns, periodic messaging.

• Corporate Communications• PR, reputation of company and stakeholders

• Research• Audience definition, demographics, psychographics

Data Mining Meets Engineering


Articulating the Problem

“Marketing analysts need to understand the impact of their

campaigns and we can provide them an avenue to do so.”

- Surf and turf

“We should totally Hadoop something!”

- Knuckle sandwich


Feature Engineering

• Your concise data features are easy to grasp, but do they provide for an adequate model?

• Your 600-dimension model is totally awesome, but does it scale?

• How much is “good enough”?


Proposing Solutions to the Business

• Understand scale issues.

• Provide alternatives: • There is no such thing as a perfect system.

• Communicate clearly about real and opportunity costs.


The Hazards of Third Party Data

• Data might not be available forever.

• Vendors might change terms.

• Entrenchment can impede growth/change due to poor quality over time (data sources can decay, vendors may slack on maintenance).


Bonini’s Paradox


Productionalizing Prototypes

• Isn’t that a fancy word?

• Strike balance between awesome and simple.• This is almost impossible to get right.• Even if you get it right once, it won’t last.

• Better for everybody if you give me as simple a mechanism as possible.


Expanding and Maintaining 1

• Data drift• How does data change organically over time?

• Bit rot• Does anybody even remember how to refit the model?

• Split maintenance• Keeping the research model up to date with the

production model never happens.


Expanding and Maintaining 2

• Horizontal expansion exposes original scope assumptions.

• “We have it in English. What do you mean we can’t get it in Swahili?”

• Value trumps veracity. Sacrifices of purity cause degradation.

• Business needs results in accretion of surrounding goo.


Document Tone: “NLP” versus “Statistical”

• NLP/Probablistic Grammars:• Effective.• Slow.• Costly reference grammars. Consider a vendor.

• Vector space modeling (term vectors/n-grams)• Very fast at runtime.• Work best with lots of training data.• Can fit yourself, so long as you can afford to maintain it.

Language Detection

Engineering Case Study


Language-Detection


Language-Detection: Features

• Supports 53 languages.

• Fitted on Wikipedia corpora.

• Classic “one-versus-all” classification.


Language-Detection: Mechanism

• Determines the frequency with which n-grams of 1-3 characters appear inside of a labeled corpus.

“To what extent does each 1-3 character n-gram

participate in a label?”

"tho":134583,"thr":87801,"the":3415279,"thi":110969,"tha":240340


Language-Detection: Practicalities

• Downsides?

• Twitter and Facebook!

• Letter casing (“I love you” versus “i love you”).

• Mixed-language documents (e.g. Chinese documents with English words).

Delta Airlines and “Needle Sandwiches”

PR Case Study:


Overview

• Airline passengers found sewing needles in sandwiches.

• Airline attempted to redirect the conversation and measure the results.

• Visible tracked this event in social media.


Delta Airlines: Needle Sandwiches

Purchased a refinery to reduce fuel costs

Passengers found needles in their on-flight sandwiches

Free tickets given away as a promotion

Prominent terms at a week view.

Prominent terms at a

month view.

Prominent terms at a

three month view.


Delta Volumes Over Time

Purchased a refinery to reduce fuel costs

Needles found in on flight Turkey Sandwiches

Free tickets given away as a promotion


Delta Volumes Over Time


Month View


3 Month View


PR Case Study: Conclusion

• Contest didn’t pay off in the long term.

• Attempts to redirect the conversation may be ham-fisted.

• Thoughts? Conjecture?


Conclusion

Questions?

Thank Youwww.visibletechnologies.com

[email protected]

Twitter: @Visible

Phone: (888) 852-0320

data mining & engineering

Technology

visible technologies

party data data

data mining

data change

data drift

time data sources

concise data features

huge data sets