A Program in Python, consisting of 3 files. This program takes the transcript from a Donald Trump speech, makes a word count dictionary to analyse word frequency, and inputs the speech into a Markov-Chain generator. This produces pseudo-random text from the speech, which is made into sonnets and tweeted line by line.

Requests the Wall Street Journal URL containing the speech, parses the HTML and writes the speech to a text file, Trump_speech.txt. (NB - the speech is no longer available from the website - due to a paywall. trump_dict and markov_gen work using the Trump_speech.txt file saved in this project. Code for this file left, as an example of using python to parse a website.)

Reads the text file, Trump_speech.txt, makes a word count dictionary and prints out the top 50 most common words over 4 letters long, and looks up specific word counts.

Uses MarkovChain class from cc_markov file (cloned from https://github.com/Codecademy/markov_python ) - generates pseudo-random text from Trump Speech. Converts the output into sonnet form. Tweets a Trump sonnet line by line using twitter API.