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PVLL:  Message scheduling,  canceling, 
sharing,  and analytics

    
  
   

Greg From Tlncler

Q How long you each lane ...
INCOMING MESSAGES

S ""”‘
Assign ‘FLAVOR’
 to conversation

Um. ... ho| d on.  Call me.
DATA PRODUCT

Message
sentiment

Heyll Meet for dinner?  [:1

Ci Yes!  Would love to
Lolo in the mission?  Q
E] Um. ... ho...
INCOMING MESSAGES

Aaa! ! Aaaa aaa aaaaaa? 
Aaa!  Aaaaa aaaa aa
Aaaa aa aaa aaaaaaa? 

Aa. ... aaaa aa.  Aaaa aa. 

3 Mill...
INCOMING MESSAGES

Aaa! ! Aaaa aaa aaaaaa? 
Aaa!  Aaaaa aaaa aa
Aaaa aa aaa aaaaaaa? 

Aa. ... aaaa aa.  Aaaa aa. 

3 Mill...
1. UNSUPERVISED LEARNING TO LABEL MESSAGES
2. FEATURE EXTRACTION AND DESIGN

“Aaa aa aaaaa aaa. ..AAAA? !?! ”

MESSAGE SPECIFIC

I 1. Number of words

I 2. Number of...
3. SUPERVISED LEARNING

Features Label

Random guess . ... ... . . .7.7%
Biased guess . ... ... .. . .18.7%
k-nearest neig...
4. REAL-TIME PROCESSING

Conversation assignment: 

Hidden Markov Model
using Baum-Welch
and F-B algorithms
School oi the An lnniluln
0' (Mayo

ART + SCIENCE
Insight_Demo
Insight_Demo
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Insight Demo - Feb. 2015

Assigning sentiment to text message conversations: A partnership with PVLL

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Insight_Demo

  1. 1. PVLL: Message scheduling, canceling, sharing, and analytics Greg From Tlncler Q How long you each lane lo respond . _ HI! I. ) Set Dale I need to ta| k!! -<_a'-7 I2JI567_COO qwertyulop 'Ul§II'() asdlghjkl 1z'xVcv-b'n’rr; ¢ V I -l lIl'-l "‘ <. .-. Q g: . 4" KEEGAN KELSEY / INSIGHT DEMO
  2. 2. INCOMING MESSAGES S ""”‘ Assign ‘FLAVOR’ to conversation Um. ... ho| d on. Call me.
  3. 3. DATA PRODUCT Message sentiment Heyll Meet for dinner? [:1 Ci Yes! Would love to Lolo in the mission? Q E] Um. ... ho| d on. Call me. Updated conversation sentiment
  4. 4. INCOMING MESSAGES Aaa! ! Aaaa aaa aaaaaa? Aaa! Aaaaa aaaa aa Aaaa aa aaa aaaaaaa? Aa. ... aaaa aa. Aaaa aa. 3 Million Messages: Anonymized and masked!
  5. 5. INCOMING MESSAGES Aaa! ! Aaaa aaa aaaaaa? Aaa! Aaaaa aaaa aa Aaaa aa aaa aaaaaaa? Aa. ... aaaa aa. Aaaa aa. 3 Million Messages: Anonymized and masked! / / Natural *EMOTlCON* Processing
  6. 6. 1. UNSUPERVISED LEARNING TO LABEL MESSAGES
  7. 7. 2. FEATURE EXTRACTION AND DESIGN “Aaa aa aaaaa aaa. ..AAAA? !?! ” MESSAGE SPECIFIC I 1. Number of words I 2. Number of capital letters I 3. Total punctuation I 4. Number of question marks I 5. Number of exclamation marks USER SPECIFIC 1. Average daily texts 2. Average word count per message
  8. 8. 3. SUPERVISED LEARNING Features Label Random guess . ... ... . . .7.7% Biased guess . ... ... .. . .18.7% k-nearest neighbor. ..26.7°/ o random forest . ... ... .. 31.5%
  9. 9. 4. REAL-TIME PROCESSING Conversation assignment: Hidden Markov Model using Baum-Welch and F-B algorithms
  10. 10. School oi the An lnniluln 0' (Mayo ART + SCIENCE

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