This is a basic Twitter data miner written in Python. It utilizes the Twython, Pandas, Numpy, Matplotlib, Re, Textblob libraries to extract and analyze Twitter data based on a specific search query.
By collecting and organizing the data by chosen time period we are able to visualize the frequency of use of the search term per period as well as assign a positive or negative sentiment to the period based on natural language processing analysis.
We are also able to gain some suggestion as to the rising or falling popularity of the search term by fitting a regression line across the periods, where the slope of the line can suggest an accelerating or decelerating trend.
We are also able to aggregate a list of related hashtags that are used along with the search query.
Since this simple data miner utilizes the Twitter standard search API we are limited to 7 days of historic data and completeness is not guaranteed, it is enough for us to build an example that illustrates the possibilities available by mining public opinion through the medium of Twitter.