This past week we’ve been working on creating visualizations of the data we’ve collected and starting to prepare it to be put on the R Shiny Snap app.
Below is an analysis we created for understanding SNAP sentiment over time. The y axis shows the Vader score, and the x axis shows the date. When you hover over one of the bars, you will be able to see the most frequent words from that time frame. This is important because positive sentiment can be due to many things – for example, people may be speaking positively about budget cuts to SNAP, or they be speaking positively about SNAP itself. Showing the most frequent words will help to tease out the meaning.
Additionally, we continued to work further on our map of news outlets. We included information about sentiment and then created a hotspot map of sentiment. In the map below, blue is a cold spot (negative about SNAP), and red is a hot spot (positive about SNAP). We are also planning on adding the top words to start extracting the meaning of this sentiment.
Finally, our politician tracking tool is coming along nicely. The data has been cleaned and is being displayed in RShiny. Below is a screenshot of the application: you are able to choose if are researching a senator or house of representatives member, and then you will choose the specific representative. Going forward we will include more detail on the bills and the representatives.