In this blog post, I will learn you how you can mine opinions about companies from news articles. I will share how I scraped thousands of news articles in a few minutes and how one could classify the opinion expressed in the titles of the news articles. This information could be used for example to help with watching competitors of a company or to predict global trends.
MLPs (Multi-Layer Perceptrons) are great for many classification and regression tasks. However, it is hard for MLPs to do classification and regression on sequences. In this Python deep learning tutorial, a GRU is implemented in TensorFlow. Tensorflow is one of the many Python Deep Learning libraries.
By the way, another great article on Machine Learning is this article on Machine Learning fraud detection. If you are interested in another article on RNNs, you should definitely read this article on the Elman RNN.
How can we use machine learning to predict stockprices? In this tutorial we will make Python scripts for doing sentiment analysis on Tweets and it is explained how to use it for making predictions.
As an example, suppose we had €1000,- at the first of January of 2014 and suppose we could use the algorithm which is described in this tutorial. Then it would generate €2901,- in total on the 22th of February, 2017! The total amount of money (cash + investments) is shown in the next figure:
Despite the patience you need to have, it will be worth the waiting time eventually. As mentioned in , moods in tweets are a good indication of the movement of closing prices on a stock market. In this article, we will only predict how positive or how negative a tweet is. But it turns out that this is giving predictive signals which is accurate enough for our purposes.
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