The document discusses using machine learning platforms like Amazon Machine Learning and IBM Watson to monitor social media. It provides examples of using these platforms to build a social media monitor that can classify tweets as requiring a response or not, and extend this to determine sentiment, topics, and sender personality. It also discusses gathering additional website data using WordPress analytics to improve predictions.

1. Monitoring social media the
“smart” way.
SETTING UP A MACHINE
LEARNING PLATFORM

2. TODAY’S EXAMPLES
● “Classic” learning — Social media monitor
○ 10xnation.com/social-customer-care-amazon-machine-learning
● “Deep” learning — Extending the social monitor
○ 10xnation.com/social-customer-care-ibm-watson
● Gathering data — Website traffic
○ 10xnation.com/wordpress-analytics

3. “CLASSIC” LEARNING
Example #2: A social media monitor
using Amazon Machine Learning

4. THE PROBLEM
Too many social media posts to track and read.
Many of our customers/prospects are feeling
neglected because we don’t have the resources to
read and respond to all of them.
Need a way to filter them down to only the ones
where the sender is expecting a response.

5. THE QUESTION
Is this tweet actionable?

6. DETERMINING THE ANSWER
Is the sender…
● Making a request
● Asking a question
● Reporting a problem
● Angry or Unhappy
● None of the above
Actionable?
Yes (1)
Yes (1)
Yes (1)
Yes (1)
No (0)

7. THE SOLUTION
Use Amazon Machine Learning to analyze a Twitter
stream in real-time and make a determination about
whether or not a tweet requires a response.
(binary classification: yes or no)
Then route the positives to a customer service agent.
Based on: github.com/awslabs/machine-learning-samples/tree/master/social-media

8. Speech
to Text
Sentiment
Analysis
Actionable
Analysis
Customer
Support
PREDICTIVE ENGAGEMENT
Customer
support call
recordings
Convert audio
into text
Analyze for
mood keywords
Determine if
response is required
Reach out to
customer/prospect
Blog & community comments
Social media mentions
Press & blog coverage
Customer support chat
Product reviews
Inbound emails

9. BREAK IT DOWN
Twitter
API
Mechanical
Turk
Amazon
Kinesis
Amazon
Machine
Learning
Amazon
Lamda
Model
Amazon
SNS
Customer
Service
Labels training data
Responds to tweets
Forwards “actionable”
tweets to support team
Captures Twitter stream
Relays tweets between
Kinesis, ML & SNS
Classifies tweets as
“actionable” or not

10. END RESULT
Your staff doesn’t have to read each tweet, and
your customers feel appreciated and happy.

11. THE FEATURES
created_at_in_seconds
description
favorite_count
favorited
favourites_count
followers_count
friends_count
geo_enabled
in_reply_to_screen_name
in_reply_to_status_id
in_reply_to_user_id
location
r.created_at_in_seconds
r.description
r.favorite_count
r.favorited
r.favourites_count
r.followers_count
r.friends_count
r.geo_enabled
r.in_reply_to_screen_name
r.in_reply_to_status_id
r.in_reply_to_user_id
r.location
r.retweet_count
r.screen_name
r.sid
r.statuses_count
r.text
r.time_zone
r.uid
r.user.name
r.utc_offset
r.verified
retweet_count
screen_name
sid
statuses_count
text
time_zone
uid
user.name
utc_offset
verified
trainingLabel

12. TRAINING DATA
docs.google.com/spreadsheets/d/1Vgo67s8swCeE9_1G9uXOxiRY0l4OJrwFSn4XXpXilMI/edit

13. Warning: Live social media content.

14. 10xnation.com/social-customer-care-amazon-machine-learning
STEP BY STEP GUIDE
● Step 1: Requirements
● Step 2: Gather training data
● Step 3: Prepare raw tweets for labeling
● Step 4: Submit job to Mechanical Turk
● Step 5: Format labeled data
● Step 9: Upload training data to S3
● Step 7: Generate the Model
● Step 8: Configure Machine Learning
● Step 9: Configure Kinesis
● Step 10: Configure IAM
● Step 11: Configure SNS
● Step 12: Configure Lambda
● Step 13: Configure Twitter
● Step 14: Fire it up

15. “DEEP” LEARNING
Example #2: A social media monitor
using IBM Watson

16. EXTENDING THE SOCIAL MONITOR
Let’s make our new social media monitor even better…
● Wrap a UI around it
● Pre-populate a tweet response
● Categorizes topic of each tweet
● Determine sentiment of each tweet
● Provide insight into personality of sender

17. THE SOLUTION
Use IBM Watson to analyze a Twitter stream in
real-time and determine…
● Sentiment
● If response required
● Type of response required
Based on: github.com/watson-developer-cloud/social-customer-care

18. Speech
to Text
Sentiment
Analysis
Actionable
Analysis
Customer
Support
PREDICTIVE ENGAGEMENT
Customer
support call
recordings
Convert audio
into text
Analyze for
mood keywords
Determine if
response is required
Reach out to
customer/prospect
Blog & community comments
Social media mentions
Press & blog coverage
Customer support chat
Product reviews
Inbound emails

19. Twitter
API
Alchemy
API
Responds to tweets
Customer
Service
User
Interface
Model
Personality
Insights
Tone
Analyzer
Natural
Language
Classifier
Analyzes sender’s prior
tweets to estimate their
personality
Sentiment analysis of
tweet stream
Classify topics in tweet stream
Analyzes sender’s prior
tweets to determine
common topics

20. END RESULT
A “smart” application that streamlines your customer
service processes on Twitter.

21. TRAINING DATA
docs.google.com/spreadsheets/d/1daGb3Kai1gN9WUgnfFnzmwavnmCaKdvdgaj2fhlvYe0/edit

22. Warning: Live social media content.

23. STEP BY STEP GUIDE
10xnation.com/social-customer-care-ibm-watson
● Step 1: Requirements
● Step 2: Configure Natural Language Classifier
● Step 3: Configure Alchemy Language
● Step 4: Configure Personality Insights
● Step 5: Configure Tone Analyzer
● Step 6: Configure Twitter
● Step 7: Train the Natural Language Classifier
● Step 8: Create the application
● Step 9: Fire it up

24. ENDLESS POSSIBILITIES
● Give customer service agents a way to provide
feedback on the system’s accuracy
● Capture the agent’s feedback and tweet responses
● Use new data to further refine prediction accuracy
● Automate more and more as system gets “smarter”

25. GATHERING DATA
Example #3: Capturing website traffic
data using WordPress

26. THE PROBLEM
To make accurate predictions and insights, we need
data. The more, the better.
But most of us don’t have much data today.

27. THE QUESTION
How can we gather more data from our website,
so we can better understand our visitors
— and predict their behavior.

28. THE SOLUTION
We’ll use WordPress for this example.
And we’ll capture as much data from it as we can.

29. STEP BY STEP GUIDE
10xnation.com/wordpress-analytics
● Step 1: Webserver Data
● Step 2: PHP Data
● Step 3: WordPress Data
● Step 4: Browsing Data

30. THOUGHTS?
Hurdles looking easier to navigate?

31. See you next month.
IT'S BEEN AN
HONOR AND A PLEASURE
chris@mohritz.co

32. UNLEASH YOUR BUSINESS
EMBRACE EXPONENTIAL
10xnation.com