OpenNLP: Predicting Stock Movements from the News

Published by pschatzmann on

In my last blog I demonstrated how to build a model that can predict if a stock is going up or down based on the news headlines using Spark MLLib. In this demo I will do the same – but with the help of OpenNLP.

The solution consists of the following components

  • OpenNLP (Text Classification)
  • My News-Digest fuctionality (which I have described in my last blogs)
  • Investor (Determination of the stock prices to calculate the labels )
  • confusion-matrix (to evaluate the Classification result)

The details can be found in the following Gist

see also:

  1. Deeplearning4j – Iris Classification Example
  2. Deeplearning4j – Recurrent Neural Networks (RNN)
  3. Tensorflow Lite Microspeach with my Audio Tools Library – The Final Solution
  4. Docker and Treafik – A Comprehensive Tutorial
  5. Serving WordPress from a Mobile Network
  6. PWM Support for my Raspberry Pico Arduino Framework
  7. An Introduction to Arduino Audio Generated by Tensorflow Lite
  8. H2O Sparkling Water – Distributed Random Forrest
  9. Random Forrest Classifier in Spark ML
  10. Docker + WordPress = Danger
Categories: Data ScienceMachine LearningQuantitative Trading

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