Visualize frequency of dictionary terms using quanteda

Visualizing the frequency of terms within a dictionary using the "quanteda" package offers valuable insights into the prevalence of specific words or concepts within a text corpus. This blog post will demonstrate how to leverage the capabilities of "quanteda" for this purpose, enabling you to uncover patterns and relationships within your data.

1. Setup and Loading Necessary Libraries:

Begin by loading the necessary libraries in R:

library("quanteda")
library("quanteda.textstats")

2. Data Preparation:

For this example, we'll use the built-in data_corpus_inaugural dataset, which contains inaugural speeches from various U.S. presidents:

toks <- data_corpus_inaugural %>% 
    tokens(remove_punct = TRUE) %>% 
    tokens_tolower() %>% 
    tokens_remove(pattern = stopwords("en"))

3. Creating a Dictionary:

Next, define a dictionary containing the terms of interest. In this case, we'll create a dictionary with two keys, "liberty" and "justice," and their associated values:

dict <- dictionary(list(liberty = c("freedom", "free"), 
                            justice = c("justice", "law")))

4. Retrieving Term Frequency Data:

To obtain the frequency data for each term in the dictionary, we'll use the following steps:

1. Use lapply() to iterate through each key in the dictionary.
2. For each key, select the corresponding terms from the tokenized corpus using tokens_select().
3. Create a document-feature matrix (dfm) using the selected terms and calculate term frequencies.
4. Bind the dictionary key to the dfm and relevant statistics, such as frequency and rank.

5. Combining Results:

Finally, we can combine the results for all keys into a single data frame using do.call(rbind, dfmat_list).

6. Output:

The output will be a data frame displaying the term frequency information for each dictionary key, including the term, frequency, rank, and document frequency:

do.call(rbind, dfmat_list)

Conclusion:

This comprehensive code demonstrates how to utilize the "quanteda" package to visualize the frequency of terms within a dictionary. By leveraging the capabilities of "quanteda," you can extract meaningful insights from your text data and gain a deeper understanding of the underlying themes and patterns within the corpus.