Gaining Insights
Lesson 1 of 1
  1. 1
    Code Consolidation
    In this unit, we’ll be adding the code that will analyze the Twitter data we retrieve from Twitter users. At the moment, however, our Celebrity Match application can be tough to read or unders…
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  2. 2
    The flatten() Function
    The analyze() function we just created will return the results from the Personality Insight (PI) API in JSON format. Whenever we use the analyze() function, we’ll need to parse and flatten the JS…
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  3. 3
    The compare() Function
    The flatten() function flattens the results from a user and store the results in a dictionary . The next step is to write a function that can compare two dictionaries (the user’s and the celebrity…
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  4. 4
    Twitter Handles
    With all of our functionality in place, we now need two Twitter users to compare. We’ll manually enter the Twitter usernames by creating empty variables that you can fill in, like so: user_handle…
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  5. 5
    Analyze
    Now that we have two Twitter usernames, we can use the analyze() function that we created earlier to retrieve some data from Twitter, like so: user_result = analyze(user_handle) celebrity_result =…
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  6. 6
    Flattening & Comparing
    Now let’s use the two functions we added earlier to format our results. First, we’ll use the flatten() function to flatten the JSON structure that the analyze() function returns, like so: #First,…
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  7. 7
    Sorting and Outputting the Results
    Finally, to view the results, we’ll have to sort them and then display the top 5 traits from the sorted results. These will be the top 5 traits you share with the celebrity you selected for the ap…
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  8. 8
    Conclusion
    Congratulations! You built a fully functional, Watson-powered application that does the following: * Queries the Twitter API * Queries the Watson Personality Insights API * Compares two bodies of …
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