Generic Short Text Classifier

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Generic Short Text Classifier

Daten & Analysen

Ham or Spam?
Generic Short Text
Classiﬁer
Romit Singhai

Some Stats
•  241 million active user accounts per month
•  5% are spam account

Some More Stats…
•  130 million mobile users
•  Two thirds of mobile users received SMS spam

Data Pipeline
Acquisition
(TwiAer
API)
Cleaning
and
Munging
Feature
Engineering
Feature
Selection
Feature
Extraction

Text Classiﬁcation

Text Classiﬁcation

Confusion Matrix

Feature Engineering

Feature Engineering

Generic Classiﬁer
•  Added SMS data to Twitter Data
o  Tweet + SMS (AUC=0.91)
•  Just meta features
o  Random Forest Classifier(Mean Accuracy 0.70)

Beneﬁts
•  Reduce operational cost
•  Improve Security
•  Supports Revenue Model

Next Steps…
•  Incorporate data from other platforms
•  Analyze and Extract more meta features
•  “Catch All” Classifier as API

Thanks
Romit Singhai
romits@gmail.com

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Generic Short Text Classifier

1. Ham or Spam? Generic Short Text Classiﬁer Romit Singhai

2. Some Stats •  241 million active user accounts per month •  5% are spam account

3. Some More Stats… •  130 million mobile users •  Two thirds of mobile users received SMS spam

4. Data Pipeline Acquisition (TwiAer API) Cleaning and Munging Feature Engineering Feature Selection Feature Extraction

5. Text Classiﬁcation

6. Text Classiﬁcation

7. Confusion Matrix

8. Feature Engineering

9. Feature Engineering

10. Generic Classiﬁer •  Added SMS data to Twitter Data o  Tweet + SMS (AUC=0.91) •  Just meta features o  Random Forest Classifier(Mean Accuracy 0.70)

11. Beneﬁts •  Reduce operational cost •  Improve Security •  Supports Revenue Model

12. Next Steps… •  Incorporate data from other platforms •  Analyze and Extract more meta features •  “Catch All” Classifier as API

13. Thanks Romit Singhai romits@gmail.com