The inspiration for the web app was learning to use natural language processing to distinguish between different public figures' communication styles and potentially reach conclusions about the process of communication from different leaders.
What it does
The user inputs an average tweet from their account; it is assumed that the user will input a tweet-length sample of writing, with hashtags or links if desired. The language sample is tested against a neural network pretrained on the 500 most recent tweets each from Donald Trump and Barack Obama. The output of the program is a politician who matches the user's language sample most closely along with the degree of confidence to which the predicted politician is correct. Of course, it is likely that percentages will be low, as the neural network expects only tweets from Obama or Trump; however, the language sample can come from the user's own tweets or the tweets of those in a famous individual.
How we built it
Challenges we ran into
Accomplishments that we're proud of
It would be interesting to build a neural network by hand to avoid the limit on pretraining examples. Unfortunately, the project was chosen at Polyhack, and despite a few initial attempts to research neural language processing, it was ultimately decided to be impractical to build our own network from scratch in such a short period of time. This would allow more examples to be testing and thus increase accuracy in both the two candidates chosen and add additional celebrities. It would also be interesting to look at the choice of people tagged in posts and to analyze whether relationship circles could accurately predict a celebrity tweet. Finally, it would be interesting to analyze the ratio of pictures tweeted to the ratio of words as it compared to celebrity communication styles. Perhaps the data could be analyzed to determine a difference between political parties or occupations.