economic_dictionary<-c('economy','unemployment','trade','tariffs') To do this, we could first create a list or вЂњdictionaryвЂќ or terms that are associated with the economy. Suppose we are doing a study of economic issues, and want to subset those tweets that contain words associated with the economy. To begin, letвЂ™s make our own dictionary of terms we want to examine from the Trump tweet dataset. There are numerous examples that we shall discuss below- some of which are more sophisticated than others. Though word frequency counts and tf-idf can be an informative way to examine text-based data, another very popular techniques involves counting the number of words that appear in each document that have been assigned a particular meaning or value to the researcher. In simpler terms, the tf-idf helps us capture which words are not only important within a given document but also distinctive vis-a-vis the broader corpus or tidytext dataset.Дictionary-Based Quantitative Text Analysis The tfidf increases the more a term appears in a document but it is negatively weighted by the overall frequency of terms across all documents in the dataset or Corpus. Now letвЂ™s see what the most unusual words are: top_tfidf% We can calculate the tf-idf for the Trump tweets databased in tidytext as follows: tidy_trump_tfidf% The metric most commonly used to identify these unusual words is вЂњTerm Frequency Inverse Document FrequencyвЂќ (tf-idf). Though we have already removed very common вЂњstop wordsвЂќ from our analysis, it is common practice in quantitative text analysis to identify unusual words that might set one document apart from the others (this will become particularly important when we get to more advanced forms of pattern recognition in text later on). Ylab('Number of Times Word Appears in Trump's Tweets')+ Theme( = element_text(angle = 90, hjust = 1))+ Trump_tweet_top_words$word <- factor(trump_tweet_top_words$word, levels = trump_tweet_top_words$word) #create factor variable to sort by frequency Now letвЂ™s make a graph of the top 20 words #select only top words Trump_tweet_top_words[-grep('https|t.co|amp|rt', Next, letвЂ™s count the top words after removing stop words (frequent words such as вЂњtheвЂќ, and вЂњandвЂќ) as well as other unmeaningful words (e.g.В https): data('stop_words') LetвЂ™s begin by loading the Trump tweets we extracted in a previous tutorial and transform them into tidytext format: load(url('')) WeвЂ™re going to work within the tidytext framework, so if you need a refresher on that, see my previous tutorial entitled вЂњBasic Text Analysis in R.вЂќ In this section, weвЂ™ll learn a few basic techniques for counting word frequencies and visualizing them.

In the early days of quantitative text analysis, word-frequency counting in texts was a common mode of analysis.

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