I have two ideas, both of which I really love but one that I think would be really interesting in terms of text analysis
The first is to keep building on my "But No Black Widow Movie" Twitter bot. I would like to increase the complexity of generated tweets; currently the tweets follow the format of "A <male Marvel character> <movie type> announced for Phase <number>, #butnoblackwidowmovie?". What I'm thinking about adding is:
A <Marvel character> sequel featuring <Marvel character>
<Marvel character or group> teams up with <Marvel character or group>
And any other variants there of. I also wanted to utilize the WTF Engine to do some fun text generation.
My other idea that I'm thinking about more strongly is in collaboration with someone in my data visualization class; we were interested in the diversity of viewpoints in the content people consume on Twitter - does social media create an echo chamber of homogenous opinion/perspective? We do this by creating a low polygon image of a person that's colored based on the variety of topics/perspectives they are exposed to on Twitter.
The user will take a selfie and give us their Twitter handle - based on that, we'll pull their list of 'friends' (people they follow according to the Twitter API). We'll pull their text descriptions and strip out the common words to get only the keywords and hashtags they use. We'll then classify those words (manually or programmatically) and generate a Voronoi diagram of their interests.
The selfie the user takes will be converted to greyscale and then tesselated into a low poly version. We'll overlay the generated Voronoi diagram on top to get color values; the more colors, the more complex and diverse the network of viewpoints the user is exposed to.