Title:	Scrape and Analyze 'Goodreads' Book Data
Version:	0.1.2
Description:	A comprehensive toolkit for scraping and analyzing book data from https://www.goodreads.com/. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from 'Goodreads' data.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	cld2, dplyr, ggplot2, httr, lubridate, magrittr, parallel, purrr, rvest, rlang, stringr, tidyr, tidytext, tm, topicmodels, utils, wordcloud2
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), withr
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://github.com/chaoliu-cl/Goodreader, http://liu-chao.site/Goodreader/
BugReports:	https://github.com/chaoliu-cl/Goodreader/issues
NeedsCompilation:	no
Packaged:	2024-10-23 17:21:27 UTC; chaoliu
Author:	Chao Liu [aut, cre, cph]
Maintainer:	Chao Liu <chaoliu@cedarville.edu>
Depends:	R (≥ 3.5.0)
Repository:	CRAN
Date/Publication:	2024-10-24 10:31:07 UTC

Perform sentiment analysis on book reviews with negation handling

Description

This function takes the output from scrape_reviews and performs sentiment analysis, including basic negation scope detection.

Usage

analyze_sentiment(reviews_df, lexicon = "afinn")

Arguments

Value

A data frame with sentiment scores for each review.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the scrape_reviews function
reviews <- scrape_reviews(temp_file, num_reviews = 5, use_parallel = FALSE)

# Check if reviews were successfully scraped
if (nrow(reviews) > 0) {
  # Perform sentiment analysis
  sentiment_results <- analyze_sentiment(reviews, lexicon = "afinn")

  # Display the first few rows of the results
  print(head(sentiment_results))
} else {
  cat("No reviews found. Cannot perform sentiment analysis.\n")
}

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

Calculate average sentiment score per book

Description

This function calculates the average sentiment score for each book.

Usage

average_book_sentiment(sentiment_df)

Arguments

Value

A data frame with average sentiment scores for each book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the scrape_reviews function
reviews <- scrape_reviews(temp_file, num_reviews = 5, use_parallel = FALSE)

# Check if reviews were successfully scraped
if (nrow(reviews) > 0) {
  # Perform sentiment analysis
  sentiment_results <- analyze_sentiment(reviews, lexicon = "afinn")

  # Calculate average sentiment score per book
  avg_senti <- average_book_sentiment(sentiment_results)

  # Display the results
  print(avg_senti)
} else {
  cat("No reviews found. Cannot calculate average sentiment.\n")
}

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

Perform topic modeling on preprocessed reviews

Description

This function performs LDA topic modeling on the preprocessed reviews.

Usage

fit_lda(dtm, k, method = "Gibbs")

Arguments

Value

An LDA model

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 model summary
print(lda_model)

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

Create word cloud for topics

Description

This function creates a word cloud for each topic.

Usage

gen_topic_clouds(model_output, n = 50)

Arguments

Value

A list of ggplot objects, where each element represents a word cloud 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 = 30, use_parallel = FALSE)

# Model topics
topic_results <- model_topics(reviews, num_topics = 3, num_terms = 50, english_only = TRUE)

# Generate word clouds for each topic
wordcloud_plots <- gen_topic_clouds(topic_results, n = 20)

# Display the word cloud for the first topic
if (interactive()) {
  print(wordcloud_plots[[1]])
}

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

Get Author Information from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the author information for each book.

Usage

get_author_info(file_path)

Arguments

Value

A named list where each element contains the author information for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Run the function
author_info <- get_author_info(temp_file)
print(author_info)
# Clean up: remove the temporary file
file.remove(temp_file)

Save Book IDs to a Text File

Description

This function retrieves the book_id values from the input_data and saves them to a specified text file.

Usage

get_book_ids(input_data, file_name)

Arguments

Value

No return value, the function writes the book_id values to a text file.

Examples

# Create sample data
books <- data.frame(title = c("Hamlet", "The Hunger Games", "Jane Eyre"),
                    book_id = c("1420", "2767052", "10210")
                    )
# Create a temporary file path
temp_file <- file.path(tempdir(), "bookids.txt")

# Run the function
get_book_ids(books, temp_file)

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

Get Book Summary from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the summary for each book.

Usage

get_book_summary(file_path)

Arguments

Value

A named list where each element contains the summary for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Run the function
book_summary <- get_book_summary(temp_file)
print(book_summary)
# Clean up: remove the temporary file
file.remove(temp_file)

Get Format Information from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the format information for each book.

Usage

get_format_info(file_path)

Arguments

Value

A named list where each element contains the format information for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the function
format_info <- get_format_info(temp_file)
print(format_info)

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

Get Genres for Books from Goodreads

Description

This function reads book IDs from a file, fetches the corresponding Goodreads pages, and extracts the genres for each book.

Usage

get_genres(file_path)

Arguments

Value

A named list where each element corresponds to a book ID and contains a character vector of genres for that book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the function
genres <- get_genres(temp_file)

# Display the results
print(genres)

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

Get Number of Pages from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the number of pages for each book.

Usage

get_num_pages(file_path)

Arguments

Value

A named list where each element contains the number of pages for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Run the function
num_pages <- get_num_pages(temp_file)
print(num_pages)
# Clean up: remove the temporary file
file.remove(temp_file)

Get Published Time from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the published time for each book.

Usage

get_published_time(file_path)

Arguments

Value

A named list where each element contains the book information for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Run the function
published_times <- get_published_time(temp_file)
print(published_times)
# Clean up: remove the temporary file
file.remove(temp_file)

Get Rating Distribution from Goodreads

Description

This function takes a file path containing Goodreads book IDs and retrieves the rating distribution for each book.

Usage

get_rating_distribution(file_path)

Arguments

Value

A named list where each element contains the rating distribution for a book.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the function
rating_distributions <- get_rating_distribution(temp_file)
print(rating_distributions)

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

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

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)

Visualize topic distribution

Description

This function creates a heatmap of the topic distribution across documents.

Usage

plot_topic_heatmap(model_output)

Arguments

Value

A ggplot object representing the topic distribution heatmap.

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 = 10, use_parallel = FALSE)

# Model topics
topic_results <- model_topics(reviews, num_topics = 2, num_terms = 5, english_only = TRUE)

# Visualize topic distribution
plot_topic_heatmap(topic_results)

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

Visualize topic prevalence

Description

This function creates a bar plot of the overall prevalence of each topic.

Usage

plot_topic_prevalence(model_output)

Arguments

Value

A ggplot object representing the bar plot of topic prevalence.

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 = 10, use_parallel = FALSE)

# Model topics
topic_results <- model_topics(reviews, num_topics = 2, num_terms = 5, english_only = TRUE)

# Visualize topic distribution
plot_topic_prevalence(topic_results)

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

Visualize top terms for each topic

Description

This function creates a bar plot of the top terms for each topic.

Usage

plot_topic_terms(model_output, n = 10)

Arguments

Value

A ggplot object representing the bar plot of top terms for each 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 = 10, use_parallel = FALSE)

# Model topics
topic_results <- model_topics(reviews, num_topics = 2, num_terms = 5, english_only = TRUE)

# Visualize top terms for each topic
plot_topic_terms(topic_results, n = 5)

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

Preprocess review text for topic modeling

Description

This function preprocesses the review text by optionally filtering non-English reviews, removing punctuation, converting to lowercase, removing stopwords, and stemming.

Usage

preprocess_reviews(reviews, english_only = TRUE)

Arguments

Value

A list containing the following elements:

• corpus: The preprocessed corpus object.

• dtm: The document-term matrix.

• filtered_reviews: The 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)

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

# Print the document-term matrix
print(preprocessed$dtm)

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

Replace special characters and remove non-ASCII characters

Description

Replace special characters and remove non-ASCII characters

Usage

replace_special_chars(x)

Arguments

Value

A character vector with special characters replaced and non-ASCII characters removed

Scrape book details from Goodreads

Description

This function scrapes details of books using their IDs from Goodreads.

Usage

scrape_books(book_ids_path, use_parallel = FALSE, num_cores = 4)

Arguments

Value

A data frame containing scraped book details.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the function (with a small delay to avoid overwhelming the server)
result <- scrape_books(temp_file, use_parallel = FALSE)
print(head(result))
# Clean up: remove the temporary file
file.remove(temp_file)

Scrape book reviews from Goodreads

Description

This function scrapes book reviews from Goodreads based on provided book IDs.

Usage

scrape_reviews(
  book_ids_path,
  num_reviews = 30,
  use_parallel = FALSE,
  num_cores = 4
)

Arguments

Value

A data frame containing scraped review information.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)
# Run the function (with a small number of reviews to keep the example quick)
reviews <- scrape_reviews(temp_file, num_reviews = 5, use_parallel = FALSE)
print(head(reviews))
# Clean up: remove the temporary file
file.remove(temp_file)

Search Goodreads

Description

This function searches books on Goodreads.

Usage

search_goodreads(
  search_term,
  search_in = c("title", "author"),
  num_books = 10,
  sort_by = "ratings"
)

Arguments

Value

A data frame of search results.

Examples

search_goodreads("parenting", search_in = "title", num_books = 2)

Create a histogram of sentiment scores

Description

This function creates a histogram of sentiment scores for all reviews.

Usage

sentiment_histogram(sentiment_df)

Arguments

Value

A ggplot object representing the histogram.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the scrape_reviews function
reviews <- scrape_reviews(temp_file, num_reviews = 10, use_parallel = FALSE)

# Check if reviews were successfully scraped
if (nrow(reviews) > 0) {
  # Perform sentiment analysis
  sentiment_results <- analyze_sentiment(reviews, lexicon = "afinn")

  # Create histogram of sentiment scores
  sentiment_hist <- sentiment_histogram(sentiment_results)

  # Display the plot
  print(sentiment_hist)

  # Optionally, save the plot
  # ggsave("sentiment_hist.png", sentiment_hist, width = 8, height = 6)
} else {
  cat("No reviews found. Cannot create sentiment histogram.\n")
}

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

Plot sentiment trend over time

Description

This function plots the average sentiment score over time.

Usage

sentiment_trend(sentiment_df, time_period = "month", show_smooth_trend = FALSE)

Arguments

Value

A ggplot object representing the sentiment trend.

Examples

# Create a temporary file with sample book IDs
temp_file <- tempfile(fileext = ".txt")
writeLines(c("1420", "2767052", "10210"), temp_file)

# Run the scrape_reviews function
reviews <- scrape_reviews(temp_file, num_reviews = 10, use_parallel = FALSE)

# Check if reviews were successfully scraped
if (nrow(reviews) > 0) {
  # Perform sentiment analysis
  sentiment_results <- analyze_sentiment(reviews, lexicon = "afinn")

  # Create histogram of sentiment scores
  senti_trend <- sentiment_trend(sentiment_results)

  # Display the plot
  print(senti_trend)

  # Optionally, save the plot
  # ggsave("senti_trend.png", senti_trend, width = 8, height = 6)
} else {
  cat("No reviews found. Cannot create sentiment trend\n")
}

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

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

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)