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by Mark Sturdevant, Anna Quincy, Tyler Andersen | Published July 27, 2017
AnalyticsArtificial intelligenceData scienceObject StoragePythonRetail
Read this in other languages: 한국어.
Combine the power of a Jupyter Notebook, PixieDust, and IBM Watson™ cognitive services to glean useful marketing insight from a vast body of unstructured Facebook data. To help improve brand perception, product performance, customer satisfaction, and audience engagement, take data from a Facebook Analytics export, enrich it with Watson Visual Recognition, Natural Language Understanding, and Tone Analyzer, and create interactive charts to outline your findings. Credit goes to Anna Quincy and Tyler Andersen for providing the initial notebook design.
We start with data exported from Facebook Analytics and enrich that data with Watson APIs.
We will use the enriched data to answer questions like:
These types of insights are beneficial for marketing analysts interested in understanding and improving brand perception, product performance, customer satisfaction, and audience engagement. It is important to note that this pattern is meant to be used as a guided experiment, rather than an application with one set output.
The standard Facebook Analytics export features text from posts, articles, and thumbnails, along with standard Facebook performance metrics, such as likes, shares, and impressions. This unstructured content is then enriched with Watson APIs to extract keywords, entities, sentiment, and tone.
After data is enriched with Watson APIs, there are several ways to analyze it. The Data Science Experience provides a robust yet flexible method of exploring the Facebook content.
This pattern provides mock Facebook data, a notebook, and comes with several pre-built visualizations to get you started with uncovering hidden insights. When you complete this pattern, you will understand how to:
Ready to put this code pattern to use? Complete details on how to get started running and using this application are in the README.
March 7, 2019
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