ABSTRACT
Well-known examples are spam filtering, cyber-crime prevention, counter-terrorism and sentiment analysis. In this chapter, the authors learn how to generate useful numerical summaries from text data to which they can apply some of the powerful data visualization and analysis techniques the people have learned. To learn more about text mining in R, the authors recommend the Text Mining with R book by Julia Silge and David Robinson. Note that the people use extract to remove the Twitter for part of the source and filter out retweets. The first step in sentiment analysis is to assign a sentiment to each word. The bing lexicon divides words into positive and negative sentiments.
