I blogged the process in Passion for Data blog. There are also the word cloud images.
As MIT shootings started at 18th April 2013, I hacked a quick Twitter bot to keep track of hashtag #mitshooting. After 10 hours, I wordclouded the data.
mit.py Every 15 seconds, the script fetches 15 tweets containing the hashtag #mitshooting and records the tweet and the username to database
analyse-mit.py The script fetches the tweets from the database and does cleaning (ignores couple of known spams), removes stopwords listed in stopwords.txt, removes punctuation, hashtags, mentions and links.
The script also creates hourly frequency files as well as combined frequency file for the entire 10 hours.
cloud_mit.r Read the frequency table and uses wordcloud library to create nice looking word clouds.
hourly_cloud.r Reads one frequency file per hour and creates corresponding word cloud
If you have any ideas to improve the code, feel free to fork and make a pull request to contribute. Or you can contact me via Twitter @hamatti.
Copyright (C) 2013 Juha-Matti Santala
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