Goodreader Quick Start Guide

Installing and loading the package

Install the package:

install.packages("Goodreader")

And load the package:

library(Goodreader)

Searching for Books on Goodreads

The search_goodreads() function allows you to search for books on Goodreads based on various criteria.

The code below searches for books that include the term “parenting” in the title and returned 10 books sorted by readers’ ratings

parent_df <- search_goodreads(search_term = "parenting", search_in = "title", num_books = 10, sort_by = "ratings")

summary(parent_df)
##   title              author            book_id         
## Length:10          Length:10          Length:10         
## Class :character   Class :character   Class :character  
## Mode  :character   Mode  :character   Mode  :character  

##     url               ratings     
## Length:10          Min.   : 8427  
## Class :character   1st Qu.:11744  
## Mode  :character   Median :13662  
##                    Mean   :19757  
##                    3rd Qu.:13784  
##                    Max.   :69591

You can also search author’s name:

search_goodreads(search_term = "J.K. Rowling", search_in = "author", num_books = 5, sort_by = "ratings")

The search_goodreads() function includes a sort_by that sorts the results either by ratings or published_year:

search_goodreads(search_term = "J.K. Rowling", search_in = "author", num_books = 5, sort_by = "published_year")

Scrape book metadata and reviews

After the books are found, save their IDs to a text file. These IDs are used for extracting book metadata and reviews:

get_book_ids(input_data = parent_df, file_name = "parent_books.txt") #the book IDs are now stored in a text file named “parent_books”

Book metadata can then be scraped:

parent_bookinfo <- scrape_books(book_ids_path = "parent_books.txt", use_parallel = FALSE)

To speed up the scraping process: Turn on the parallel process: use_parallel = TRUE Specify the number of cores for the parallel process (e.g., `num_cores = 8)

parent_bookreviews <- scrape_reviews(book_ids_path = "parent_books.txt", num_reviews = 10, use_parallel = FALSE) #users can also turn on parallel process to speed up the process

Conduct sentiment analysis

The analyze_sentiment() function calculates the sentiment score of each review based on the lexicon chosen by the user. Available options for lexicon are afinn, bing, and nrc. Basic negation scope detection was implemented (e.g., not happy is labeled as negative emotion and is assigned with a negative score).

sentiment_results <- analyze_sentiment(parent_bookreviews, lexicon = "afinn")

The average_book_sentiment() function calculates the average sentiment score for each book.

ave_sentiment <- average_book_sentiment(sentiment_results)
summary(ave_sentiment)
##    book_id          avg_sentiment  
##  Length:10          Min.   : 4.40  
##  Class :character   1st Qu.: 7.25  
##  Mode  :character   Median :12.86  
##                     Mean   :12.95  
##                     3rd Qu.:14.65  
##                     Max.   :27.30

The sentiment scores can be plotted as a histogram:

sentiment_histogram(sentiment_results)

Or a trend of average sentiment score over time:

sentiment_trend(sentiment_results, time_period = "year")

Perform topic modeling

Apply topic modeling to the reviews data:

reviews_topic <- model_topics(parent_bookreviews, num_topics = 3, num_terms = 10, english_only = TRUE)
## Topic 1:  
## parent, children, need, one, way, good, get, work, dont, give 
## 
## Topic 2:  
## parent, child, book, emot, feel, help, also, can, children, use 
## 
## Topic 3:  
## book, just, kid, think, read, like, time, say, realli, much

Plot the top terms by topic:

plot_topic_terms(reviews_topic)

Create a word cloud for each topic:

gen_topic_clouds(reviews_topic)

Topic 1:

Topic 2:

Topic 3:

Other utility functions

The following table shows other utility functions to extract book-related information

Function	Output	Description
get_book_ids()	Text file	Retrieve the book IDs from the input data and save to a text file
get_book_summary()	List	Retrieve the summary for each book
get_author_info()	List	Retrieve the author information for each book
get_genres()	List	Extract the genres for each book
get_published_time()	List	Retrieve the published time for each book
get_num_pages()	List	Retrieve the number of pages for each book
get_format_info()	List	Retrieve the format information for each book
get_rating_distribution()	List	Retrieve the rating distribution for each book