Setting up a Machine Learning Platform - Monitoring social media the “smart” way
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This 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 analyze tweets and determine whether they require a response from customer support. It also discusses gathering additional website traffic data using WordPress analytics to improve predictive models.
Setting up a Machine Learning Platform - Monitoring social media the “smart” way
- 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 ○ https://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.
- 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.
- 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. UNLEASH YOUR BUSINESS EMBRACE EXPONENTIAL 10xnation.com