What it is

Hackathon project that scraps the web for news from famous outlets. The news are then analyzed by IBM's Watson for dominant tone and sentiment-heavy sentences. The results are then displayed online in an easy-to-digest way.

What it does

Flavored News pulls news articles in several news categories from several prominent news providers--currently, CNN, BBC, and Fox, with the framework set in place to allow extension to more. It scrapes the articles and pulls out the important parts--title, author, date, body text--and displays their summaries in a dashboard feed. The features of the feed are as follows:

• News articles are colored to indicate the dominant emotion in their text: joy, anger, sadness, disgust

• Users can select a story's summary to view its breakdown in more detail:

  • The body of the article
  • A link to the article at its source
  • A metric indicating how strongly emotional it is
  • Indication of the most strongly emotional sentences in the article
• Users can select to view a bubble map visualizing emotional heat in the world over time (emotional intensity plotted against month, with data points scaled to reflect intensity with their size as well).

What we used

The web scrapper was written in Python, along with the interaction with IBM's Watson. The web client, which I was in charge of, was built with Meteor. For demo purposes, the project was deployed to MDG's Galaxy. For later deployment, we used Azure.