Update: Permission is required for using Facebook Graph API to get information from public pages not owned by yourself

Facebook Analysis using Rfacebook Package

The Rfacebook package is authored and maintained by Pablo Barbera and Michael Piccirilli

This demo will provide a brief introduction in

• setting up and using the Rfacebook package to scrape public pages posts and comments.
• performing a simple lexicon-based sentiment analysis
• performing text frequency analysis with the use of word cloud will be applied
• plotting a simple trend analysis based on the page's "likes", "shares" and "comments"
• creating a heatmap of the page posts by day and time
• and the page's posting frequency.

Codes Walkthrough

1. Installing the Rfacebook package

install.packages("Rfacebook")
library(Rfacebook)

Facebook authentication

2. Getting Temporary Access Token

• Get temporary (2hrs) FB access token via: https://developers.facebook.com/tools/explorer > click on Get Token
• Replace token <- "" with your token code
• You will need Graph API v2.6 and higher to get Reactions (love, haha, wow, sad, angry)

3. Creating an App on Facebook Platform

• Create an app via: https://developers.facebook.com/
• Replace app_id="xxx" with your App ID code
• Replace app_secret="xxx" with your App Secret code

token <- "xxxxxxxxxx"
fb_oauth <- fbOAuth(app_id="xxx",app_secret="xxx",extended_permissions = TRUE)

save(fb_oauth, file="fb_oauth")
load("fb_oauth")

Get Posts from Facebook Page

4. Get the latest 5000 posts their respective Reactions information from FB Page thatsmyfairprice

ntucPosts <- getPage("thatsmyfairprice", token, n = 5000, reactions = T)

head(ntucPosts)

id	likes_count	from_id	from_name	message	created_time	type	link	story	comments_count	shares_count	love_count	haha_count	wow_count	sad_count	angry_count	post_datetime
124299741408_10150633686196409	5	124299741408	NTUC FairPrice	NA	1973-01-01T08:00:00+0000	link	https://www.facebook.com/124299741408/posts/10150633686196409/	NTUC FairPrice added a life event from 1973: Founded in 1973.	0	0	0	0	0	0	0	1973-01-01 08:00:00
124299741408_10150856484056409	8	124299741408	NTUC FairPrice	NA	1983-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856484011409/10150856484011409/?type=3	NTUC FairPrice added a life event from 1983: NTUC FairPrice Co-operative Ltd Formed.	0	1	0	0	0	0	0	1983-01-01 08:00:00
124299741408_10150856485356409	3	124299741408	NTUC FairPrice	NA	1983-06-01T07:00:00+0000	link	https://www.facebook.com/124299741408/posts/10150856485356409/	NTUC FairPrice added a life event from June 1983: Used Textbook Project Launched.	0	0	0	0	0	0	0	1983-06-01 07:00:00
124299741408_10150856487136409	6	124299741408	NTUC FairPrice	NA	1985-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856487101409/10150856487101409/?type=3	NTUC FairPrice added a life event from 1985: FairPrice Housebrand Products Introduced.	0	0	0	0	0	0	0	1985-01-01 08:00:00
124299741408_10150856489041409	1	124299741408	NTUC FairPrice	NA	1991-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856489011409/10150856489011409/?type=3	NTUC FairPrice added a life event from 1991: First Retailer to Implement Bar-Coding at Check-Out Counters.	0	0	0	0	0	0	0	1991-01-01 08:00:00
124299741408_10150856491601409	3	124299741408	NTUC FairPrice	NA	1994-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856491566409/10150856491566409/?type=3	NTUC FairPrice added a life event from 1994: Everyday Low Price (EDLP) Essential Items Introduced.	0	0	0	0	0	0	0	1994-01-01 08:00:00

Check how many posts are scraped from the FB page

nrow(ntucPosts)

2473

5. Checking the distribution of post type of this FB page

table(ntucPosts$type)

 event   link   note  photo status  video 
     7    375      4   1555    321    211 

plot(table(ntucPosts$type),ylab="Count of Post Type")

6. Let's take a look at the number of Likes & Reactions in each Post Type

install.packages("sqldf")
library(sqldf)

postType <- sqldf("select type, count(type) as total_type_count,
                  sum(shares_count) as total_shares,
                  sum(likes_count) as total_likes, 
                  sum(love_count) as total_love,
                  sum(haha_count) as total_haha,
                  sum(wow_count) as total_wow,
                  sum(sad_count) as total_sad,
                  sum(angry_count) as total_angry
                  from ntucPosts group by type")

postType

type	total_type_count	total_shares	total_likes	total_love	total_haha	total_wow	total_sad	total_angry
event	7	2	2786	10	1	0	0	0
link	375	9710	83335	560	93	484	12	18
note	4	4	26	0	0	0	0	0
photo	1555	34243	424959	3014	209	1419	172	138
status	321	2016	17255	6	47	20	8	192
video	211	38630	82989	1593	295	392	55	65

Get All Comments from the Scraped Facebook Posts

7. Loop through all the posts and get their respective comments

ntucComment <- list()
for (i in 1:length(ntucPosts$id)){
  ntucComment[[i]] <- getPost(ntucPosts$id[i], token, likes=F, comments=T)
  ntucComment[[i]][['reactions']] <- getReactions(post=ntucPosts$id[i], token)
  if (nrow(ntucComment[[i]][["comments"]]) == 0)
    ntucComment[[i]][['comments']][1,] <- c(NA,NA,NA,NA,NA,NA,NA)
}
ntucComments <- do.call(rbind, lapply(ntucComment, data.frame, stringsAsFactors=FALSE))

head(ntucComments)

post.from_id	post.from_name	post.message	post.created_time	post.type	post.link	post.id	post.likes_count	post.comments_count	post.shares_count	...	comments.likes_count	comments.comments_count	comments.id	reactions.id	reactions.likes_count	reactions.love_count	reactions.haha_count	reactions.wow_count	reactions.sad_count	reactions.angry_count
124299741408	NTUC FairPrice	NA	1973-01-01T08:00:00+0000	link	https://www.facebook.com/124299741408/posts/10150633686196409/	124299741408_10150633686196409	5	0	0	...	NA	NA	NA	124299741408_10150633686196409	5	0	0	0	0	0
124299741408	NTUC FairPrice	NA	1983-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856484011409/10150856484011409/?type=3	124299741408_10150856484056409	8	0	1	...	NA	NA	NA	124299741408_10150856484056409	8	0	0	0	0	0
124299741408	NTUC FairPrice	NA	1983-06-01T07:00:00+0000	link	https://www.facebook.com/124299741408/posts/10150856485356409/	124299741408_10150856485356409	3	0	0	...	NA	NA	NA	124299741408_10150856485356409	3	0	0	0	0	0
124299741408	NTUC FairPrice	NA	1985-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856487101409/10150856487101409/?type=3	124299741408_10150856487136409	6	0	0	...	NA	NA	NA	124299741408_10150856487136409	6	0	0	0	0	0
124299741408	NTUC FairPrice	NA	1991-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856489011409/10150856489011409/?type=3	124299741408_10150856489041409	1	0	0	...	NA	NA	NA	124299741408_10150856489041409	1	0	0	0	0	0
124299741408	NTUC FairPrice	NA	1994-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150856491566409/10150856491566409/?type=3	124299741408_10150856491601409	3	0	0	...	NA	NA	NA	124299741408_10150856491601409	3	0	0	0	0	0

nrow(ntucComments)

19518

There are 19518 rows of comments scraped but there are not actually 19518 comments being made. This is because some of the posts do not have any comments made and their respective comments columns will show NA.

Dataframe Variable Names Standardization

First, let's proceed to standardize the dataframe variable names

names(ntucComments)

1. 'post.from_id'
2. 'post.from_name'
3. 'post.message'
4. 'post.created_time'
5. 'post.type'
6. 'post.link'
7. 'post.id'
8. 'post.likes_count'
9. 'post.comments_count'
10. 'post.shares_count'
11. 'comments.from_id'
12. 'comments.from_name'
13. 'comments.message'
14. 'comments.created_time'
15. 'comments.likes_count'
16. 'comments.comments_count'
17. 'comments.id'
18. 'reactions.id'
19. 'reactions.likes_count'
20. 'reactions.love_count'
21. 'reactions.haha_count'
22. 'reactions.wow_count'
23. 'reactions.sad_count'
24. 'reactions.angry_count'

8. We will replace all . with _ instead

names(ntucComments) <- gsub("\\.", "_", names(ntucComments))
names(ntucComments)

1. 'post_from_id'
2. 'post_from_name'
3. 'post_message'
4. 'post_created_time'
5. 'post_type'
6. 'post_link'
7. 'post_id'
8. 'post_likes_count'
9. 'post_comments_count'
10. 'post_shares_count'
11. 'comments_from_id'
12. 'comments_from_name'
13. 'comments_message'
14. 'comments_created_time'
15. 'comments_likes_count'
16. 'comments_comments_count'
17. 'comments_id'
18. 'reactions_id'
19. 'reactions_likes_count'
20. 'reactions_love_count'
21. 'reactions_haha_count'
22. 'reactions_wow_count'
23. 'reactions_sad_count'
24. 'reactions_angry_count'

9. Create a function that coverts FB date format to GMT

format.facebook.date <- function(datestring) {
  date <- as.POSIXct(datestring, format = "%Y-%m-%dT%H:%M:%S+0000", tz="GMT")
}

10. Adding two new variable post_datetime & comments_datetime to ntucComments dataframe

This two variable will use the format.facebook.date function to convert the original FB date format to GMT format

ntucComments$comments_datetime <- format.facebook.date(ntucComments$comments_created_time)
ntucComments$post_datetime <- format.facebook.date(ntucComments$post_created_time)

Comments Variable Cleaning

11. Convert the comments variable column to ASCII

ntucComments$comments_message <- iconv(ntucComments$comments_message, "ASCII", "UTF-8", sub="")

12. Remove comments made by the organisation itself

ntucCommentsClean <- subset(ntucComments, comments_from_name != "NTUC FairPrice")

13. Substituting emoticons with text/descriptions

ntucCommentsClean$comments_message <- gsub(":-)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(";-)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(";)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub("=p", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":p", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":P", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub("=P", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub("=)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":-)", " happy ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub("<3", " love ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":\\(", " sad ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":-\\(", " sad ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":x", " oops ", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub(":X", " oops ", ntucCommentsClean$comments_message)

14. Substituting line breaks, tabs, digits, url and punctuations with empty space

ntucCommentsClean$comments_message <- gsub("\n", " ", ntucCommentsClean$comments_message) 
ntucCommentsClean$comments_message <- gsub("\t", " ", ntucCommentsClean$comments_message) 
ntucCommentsClean$comments_message <- gsub("\\d", "", ntucCommentsClean$comments_message) 
ntucCommentsClean$comments_message <- gsub("http[^[:blank:]]+", "", ntucCommentsClean$comments_message)
ntucCommentsClean$comments_message <- gsub("[[:punct:]]", "", ntucCommentsClean$comments_message)

15. Removing rows with less than 3 characters (I deemed to be not useful at all for text analysis)

ntucCommentsClean <- subset(ntucCommentsClean, nchar(comments_message) > 2)

Lexicon-based Sentiment Analysis

16. load positive and negative lexicons

positives = readLines("positive-words.txt")
negatives = readLines("negative-words.txt")

17. Adding custom abbreviation / internet slangs to lexicon

positives = c(positives, 'thx', 'congrats', 'luv')
negatives = c(negatives, 'wtf', 'cancellation')

18. Installing required packages

install.packages("stringr")
install.packages("dplyr")
install.packages("plyr")
library(stringr)
library(dplyr)
library(plyr)

Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)
Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)
Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
------------------------------------------------------------------------------

Attaching package: 'plyr'

The following objects are masked from 'package:dplyr':

    arrange, count, desc, failwith, id, mutate, rename, summarise,
    summarize

19. Creating a simple scoring sentiment analysis function

score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
  require(plyr)
  require(stringr)
  
  scores = laply(sentences, function(sentence, pos.words, neg.words) {
    
    sentence = gsub('[[:punct:]]', '', sentence)
    sentence = gsub('[[:cntrl:]]', '', sentence)
    sentence = gsub('\\d+', '', sentence)
    sentence = tolower(sentence)
    
    word.list = str_split(sentence, '\\s+')
    words = unlist(word.list)
    
    pos.matches = match(words, pos.words)
    neg.matches = match(words, neg.words)

    pos.matches = !is.na(pos.matches)
    neg.matches = !is.na(neg.matches)
    
    score = sum(pos.matches) - sum(neg.matches)
    
    return(score)
  }, pos.words, neg.words, .progress=.progress )
  
  scores.df = data.frame(score=scores, text=sentences)
  return(scores.df)
}

ntucSentiScores <- score.sentiment(ntucCommentsClean$comments_message,positives,negatives,.progress = "text")

  |======================================================================| 100%

hist(ntucSentiScores$score,xlab="Sentiment Score")

he histogram shows that most of the comments have a score hovering around the 0 score mark. The histogram depicts a normal distribution indicating that most of the comments are neutral with some positive and negative comments

20. Now, let's add a new variable sentiment_polar to determine the sentiment of the comment

ntucCommentsClean$sentiment <- ntucSentiScores$score
ntucCommentsClean$sentiment_polar <- ifelse(ntucCommentsClean$sentiment == 0, "Neutral", ifelse(ntucCommentsClean$sentiment > 0, "Positive", "Negative"))

head(ntucCommentsClean, n=2)

	post_from_id	post_from_name	post_message	post_created_time	post_type	post_link	post_id	post_likes_count	post_comments_count	post_shares_count	...	reactions_likes_count	reactions_love_count	reactions_haha_count	reactions_wow_count	reactions_sad_count	reactions_angry_count	comments_datetime	post_datetime	sentiment	sentiment_polar
13	124299741408	NTUC FairPrice	NA	2004-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150858221106409/10150858221106409/?type=3	124299741408_10150858221126409	1	3	0	...	1	0	0	0	0	0	2012-06-08 09:23:48	2004-01-01 08:00:00	0	Neutral
15	124299741408	NTUC FairPrice	NA	2004-01-01T08:00:00+0000	photo	https://www.facebook.com/thatsmyfairprice/photos/p.10150858221106409/10150858221106409/?type=3	124299741408_10150858221126409	1	3	0	...	1	0	0	0	0	0	2015-03-04 19:44:11	2004-01-01 08:00:00	-1	Negative

table(ntucCommentsClean$sentiment_polar) 

Negative  Neutral Positive 
    1418     8884     6986 

plot(table(ntucCommentsClean$sentiment_polar),ylab="Frequency")

mean(ntucCommentsClean$sentiment)

0.56461129106895

mean sentiment score of 0.56 indicates a mostly neutral and positive comments

You can continue to check the comments sentiments based on the post type as well
For e.g. sentiment for post_type = "link"

ntucLink <- subset(ntucCommentsClean, post_type == "link") 
hist(ntucLink$sentiment, xlab = "Sentiment Score", main = "Sentiment Histogram of NTUC Link Posts")
table(ntucLink$sentiment_polar)
mean(ntucLink$sentiment)
plot(table(ntucLink$sentiment_polar),ylab="Frequency")

Negative  Neutral Positive 
     159      908      522 

0.351793580868471

Text Frequency Analysis

21. Installing required packages for Text Analysis

Needed = c("tm", "SnowballC", "RColorBrewer", "wordcloud")  
install.packages(Needed, dependencies=TRUE)

Installing packages into '/home/nbcommon/R'
(as 'lib' is unspecified)
Warning message:
"dependencies 'Rcampdf', 'Rgraphviz', 'tm.lexicon.GeneralInquirer' are not available"also installing the dependency 'Rpoppler'

Warning message in install.packages(Needed, dependencies = TRUE):
"installation of package 'Rpoppler' had non-zero exit status"

22. Creating corpus, lower case transformation, removing of punctuation / numbers and stopwords

library(tm)
# create corpus
corpus = Corpus(VectorSource(ntucCommentsClean$comments_message))
# Conversion to lower case
corpus = tm_map(corpus, content_transformer(tolower)) 
# Removal of punctuation
corpus = tm_map(corpus, removePunctuation)
# Removal of numbers
corpus = tm_map(corpus, removeNumbers)
# Removal of stopwords
corpus = tm_map(corpus, removeWords, stopwords("english"))

Loading required package: NLP

23. Generating wordcloud

library(wordcloud)
wordcloud(corpus, random.order = F, min.freq=2, max.words=250,
          colors = brewer.pal(8, "Dark2"))

Page Trend Analysis

24. Creating function for aggregation matric

aggregate.matric <- function(metric){
  m <- aggregate(ntucPosts[[paste0(metric, "_count")]],
                 list(month = ntucPosts$month), 
                 mean)
  m$month <- as.Date(paste0(m$month, "-15"))
  m$metric <- metric
  return(m)
}

ntucPosts$timestamp <- format.facebook.date(ntucPosts$created_time)
ntucPosts$month <- format(ntucPosts$timestamp, "%Y-%m")

df.list <- lapply(c("likes", "comments", "shares"), aggregate.matric)
df <- do.call(rbind, df.list)

25. Installing ggplot2 package

install.packages("ggplot2")
install.packages("scales")
library(ggplot2)
library(scales)

Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)
Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)

Attaching package: 'ggplot2'

The following object is masked from 'package:NLP':

    annotate

26. Creating the trend plot

ggplot(df, aes(x = month, y = x, group = metric)) +
  geom_line(aes(color = metric)) +
  scale_x_date(date_breaks = "years", labels = date_format("%Y")) +
  scale_y_log10("Average count per post", breaks = c(100, 500, 1000)) +
  theme_bw() +
  theme(axis.title.x = element_blank(), axis.text.x=element_text(angle = -90, hjust = 0)) +
  ggtitle("NTUC Page CTR Performance") 

Organisation FB Post Day/Time Heatmap

27. Installing lubridate package to work with Date and Time

install.packages("lubridate")
library(lubridate)

Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)

Attaching package: 'lubridate'

The following object is masked from 'package:base':

    date

28. Creating the relevant date and time variables

ntucPosts$datetime <- format.facebook.date(ntucPosts$created_time)
ntucPosts$dayweek <- wday(ntucPosts$datetime, label=T)
ntucPosts$dayint <- wday(ntucPosts$datetime)
ntucPosts$sghour <- with_tz(ntucPosts$datetime, "Asia/Singapore")
ntucPosts$hour <- hour(ntucPosts$sghour)

29. Installing heatmap package

install.packages("d3heatmap")
library(d3heatmap)

Installing package into '/home/nbcommon/R'
(as 'lib' is unspecified)
also installing the dependency 'htmlwidgets'

30. Creating heatmap of posts by day and time

heatmapFrame <- matrix(0,nrow=24,ncol=7);
rownames(heatmapFrame) <- 0:23
colnames(heatmapFrame) <- c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat")

for (i in 1:24) {
  for (j in 1:7) {
    heatmapFrame[i,j] <- nrow(subset(ntucPosts,dayint==j & hour==i-1))
  }
}
d3heatmap(heatmapFrame, scale = "column",dendrogram = "none", color = scales::col_quantile("Blues", NULL, 5))

You can amend the codes to create and show the day and time heatmap for customers' comments using the above provided code

Organisation Posts Frequency

31. Finally, let's look at the posts frequency by the organisation

time.interval <- min(ntucPosts$datetime) %--% max(ntucPosts$datetime)
sampledays <- round(time.interval / ddays(1))

ntucPostFreq <- sampledays / nrow(ntucPosts)

ntucPostFreq

6.57096643752527

It seems that the organisation would create a new post every 6.5 days

Future Improvements

Further analysis such as:

• comparision of organisation's post timing and customers' active timing can be performed to determine if the organisation is posting contents at the right time
• topic analysis (via the use of LDA) on customers' contents can be perform to determine what topics/subjects customers are talking about or are interested in