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if you have ever heard the phrase garbage in garbage out when creating a model the same applies with text analysis we just learned how to ize which can really expose potential garbage in our text letamp;#39;s take the next step after ization and create better input text so we get better analysis before we look at some simple pre-processing steps to clean our data Iamp;#39;d like to introduce a second dataset we will be exploring 538 recently published a ton of public data one of these datasets consisted of almost three million Russian troll tweets these are tweets from bots that tweeted during the 2016 US election cycle we will explore the first 20,000 tweets as well as use some of the metadata such as the number of followers number following published date and account type to aid in some of our analysis this is a great data set for topic modeling classification task named entity recognition and others you can imagine tweets probably have a lot of garbage to show this look at the mos
