APIs are available on most modern websites, and provide an easy way to integrate the websites functionalities into your code. The API that will be focused on is the Twitter API, which which will be used to mine tweets about the event.
Social media, especially twitter, is becoming a hot topic among many investors, as its trends can often predict behavior of the stock market. This course will focus on how Twitter data can be live streamed, and will feature a worked example of the Yahoo hack, that was revealed on December 14th, 2016.
Going through the required libraries to run the code developed in this course.
Outline of how to get your credentials for using the Twitter API.
Twitter has some limitations about how far you can go back into the past to grab tweets, so if you cannot go back to December 14th, 2016, you can just follow along with a more recent date.
Matplotlib provides many great options for plotting. We will be using pyplot to visualize some of the data we gather.
Get started with contacting the Twitter API and send your first get request.
See what kind of data we get back after a get requests, and how can we parse that data?
Use additional search parameters in the requests to also define the time frame from where you want to get tweets from.
Dynamically alter the maximum time for tweets and use this process to move backwards during a day.
Use the information provided in each response to filter for English tweets, and also setup keywords to search the response for.
Identifying and picking out relevant data and storing it, so that it can be analyzed later.
Plotting the gathered data from earlier to get a time overview.
Edit the plotting setup to include date and time ticks to make the graph easier to read.
Displaying and going through the relevant data we identified earlier.
How to setup streaming from Twitter and outlook on things to do with this new data source.
I've worked for over two years in physics research and mathematical analysis. I participated in two international physics competitions, where my two teammates and I won silver and gold. My thesis was in the field of Quantum Biology, focusing on analyzing the behavior of excitons at room temperature with electronic interaction.
Due to my affinity for math and statistics from my studies in physics, I tend towards data mining, processing, and analysis, which are also the things that I find most exciting.
I enjoy learning new methods and developing my skills, and am constantly studying new literature and documentation to find exciting material that can be applied in the field of data analysis.
If you want to keep up with what else I'm doing in the fields of programming, data, and data science, you can check me out at codingwithmax.