Co-Author: Tong Li, @email4tong The Research Triangle Park (RTP), NC Kubernetes Meetup is a well-organized event and its members regularly meet every month. It has over 750 registered members. The meetup speakers are SMEs from various companies like Red Hat, IBM, Lenovo, Google and many local startups like CloudPerceptions. The last Kubernetes meetup for 2017...
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:
- What sentiment is most prevalent in the posts with the highest engagement performance?
- What are the relationships between social tone of article text, the main article entity, and engagement performance?
- Read external data into a Jupyter Notebook via DSX Object Storage and pandas DataFrame.
- Enrich unstructured data using a Jupyter Notebook and Watson Visual Recognition, Natural Language Understanding, and Tone Analyzer.
- Use PixieDust to explore data and visualize insights.
- A CSV file exported from Facebook Analytics is added to DSX Object Storage.
- Generated code makes the file accessible as a pandas DataFrame.
- The data is enriched with Watson Natural Language Understanding.
- The data is enriched with Watson Tone Analyzer.
- The data is enriched with Watson Visual Recognition.
- The enriched data can be explored with PixieDust to uncover hidden insights and create graphics to highlight them.