model_topics {Goodreader} | R Documentation |
Analyze topics in Goodreads reviews
Description
This function takes the output from scrape_reviews, preprocesses the data, performs topic modeling, and prints the results.
Usage
model_topics(reviews, num_topics = 3, num_terms = 10, english_only = TRUE)
Arguments
reviews |
A data frame containing the scraped reviews |
num_topics |
The number of topics to extract |
num_terms |
The number of top terms to display for each topic |
english_only |
A logical value indicating whether to filter out non-English reviews. Default is FALSE. |
Value
A list containing the following elements:
-
model
: The fitted LDA model object. -
filtered_reviews
: The preprocessed and filtered reviews data frame.
Examples
# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Scrape reviews
reviews <- scrape_reviews(temp_file, num_reviews = 5, use_parallel = FALSE)
# Model topics
topic_results <- model_topics(reviews, num_topics = 2, num_terms = 5, english_only = TRUE)
# Print model summary
print(topic_results$model)
# Clean up: remove the temporary file
file.remove(temp_file)
[Package Goodreader version 0.1.1 Index]