top_terms: Extract and print top terms for each topic

In Goodreader: Scrape and Analyze 'Goodreads' Book Data

Extract and print top terms for each topic

Description

This function extracts the top terms for each topic in the LDA model and optionally prints them.

Usage

top_terms(lda_model, n = 10, verbose = TRUE)

Arguments

lda_model	An LDA model
n	The number of top terms to extract for each topic
verbose	Logical; if TRUE, print the top terms to the console (default is TRUE)

Value

A list of character vectors, each containing the top terms for a topic.

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)

# Preprocess the reviews
preprocessed <- preprocess_reviews(reviews, english_only = TRUE)

# Fit LDA model
lda_model <- fit_lda(preprocessed$dtm, k = 2)

# Print top terms
top_terms(lda_model, n = 5)

# Clean up: remove the temporary file
file.remove(temp_file)

Goodreader documentation built on Oct. 30, 2024, 9:11 a.m.