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FlashPoint - Build a realtime sentiment analysis processing pipeline

Design and develop a real-time processing pipeline and dashboard for the sentiment analysis of tweets and posts about our companies.

Description of the assignment

About ToThePoint Group and our subcompanies (OnTheSpot, OneDot and ToThePoint) is regularly tweeted about on many channels. We want to check these posts for sentiment analysis so we can have a visual map about how favorable or unfavorable these messages were.
We want to both check Facebook and Twitter messages where certain hashtags/keywords are used and we want to ‘catch’ events on a Kafka message bus (LinkedIn) for further processing.
After the pre-processing of the collected events, we want to stock the messages on a Hadoop backend so we can initiate a sentiment analysis on these messages. Thereafter we want to visualize this sentiment analysis on our giant leaderboard screen in our offices.

Your assignment is the design and build of this challenge


  • Iteratively design and develop a machine learning model
  • Develop a data processing pipeline
    • Pre-processing steps
    • Sentiment analysis
  • Put your application into production
  • Build a live real-time visualisation of the results you’ve found

What you will gain

  • Learn how to design an end-to-end data processing pipeline
  • AND put this in production
  • Gain knowledge about steam processing
  • Gain knowledge and experience in machine learning
  • You will get to know Hadoop
  • You will gain experience in powerful visualization libraries such as D3.js
  • That lovely feeling you get knowing your design will be effectively used in production

What you need

  • You have a shown interest in a challenging but instructive assignment
  • You’d like to explore Machine Learning and stream processing techniques
    • Using Spark, Python or Scala does not scare you at all
  • You know what ReactJS is, or are eager to learn
  • You like to learn about data visualization
  • You like to learn a heck of a lot on a relatively short period of time
Technologies you'll be using
Location of your assignment

Veldkant 33B, 2550 Kontich

Your mentor

Kevin Smeyers – Technical lead machine learning ToThePoint

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