Final Use-Case:

The final use-case of your flask app is to perform an analysis of the attached text, which is a public article from DB News. 

Your analysis should utilise visualisations and Google’s Natural Language API calls, as outlined in the python notebooks in the repo.

The architecture, APIs and visualisation design is up to your team to decide.

Extension: If your team completes the task early, consider performing combined analyses and outputs on multiple news articles from DB News.

Input Section

• Sentence Sentiment
  • << Use the existing code >>
  • Pass the sentence to the NLP engine
  • Return/ show the sentiment
• URL Sentiment
  • Enter URL into the page
  • Screen scrape the code from the URL page
  • Clean the text of markup
  • Pass the text to the MLP engine
  • Return/ show the sentiment
• File Upload Sentiment
  • Upload file
  • Parse the file for text (remove markup)
  • Pass the text to the NLP engine
  • Return/ show the sentiment

Reporting Section

• Histogram of the 5 most common words on X-axis, and sentiment on Y-axis
  • have a list of words to exclude, e.g. the, and etc
• Line graph showing the cumulative sentence of each Input - X -axis shows cumulative Sentence count, and Y axis shows the sentiment
• . .  . .?