In today's world finance is becoming increasingly about technology and data science. In that same vein, this project aims to be a proof of concept for the analyzing of social media to get the public opinion of a Stock. This idea stems from the...
This segment of the project blog explains the way I pulled the twitter data. Although this might seem like a simple task at first, the twitter api throttles the number of requests you can make at a time. After around 4000 pulls at a time, the...
This section of the project is focused on the sentiment analysis performed on the tweets themselves. The program was first used to pull and analyze Tweets, so I could get a better sense of how to clean the tweets so TextBlob can perform accurate...
This segment of the project was focused on building an idea of what the dataset looked like. By clustering our tweets into several sets, we can determine what a influential tweet looks like. currently our csv headers look like this: date text...
Before we move onto the machine learning elements, there is one more quick step to accomplish. We need to merge each individual stock datasets together so we can do analysis overall. The code to do this is pretty self explanatory so it is posted...
Only at this stage of the project was I able to bring in the thing that it was all for: predicting stock values. It was unexpectedly difficult to find a good place to pull historical stock data because a the yahoo database was depreciated for...
The next two sections will cover two main machine learning methods and my findings as I used them. Not all the code will be fully explained, but they are similar to the tutorials described in the sci-kit learn and tflearn modules this is the less...
On the other hand, unsupervised learning has a more complex method to calculating and developing a model. It took me many tries to get the parameters with the best results and even then I still have a long way to go. I simply didn't have enough...
Although this algorithm isn't ready to conquer the stock market world, it has shown that both supervised and unsupervised machine learning algorithms have the ability to predict stock market trends based on social media data to a reasonable...