https://github.com/kwokhing/rfacebook-demo
Demo on using the Rfacebook package to scrape/mine user generated contents (UGCs) from public FB pages. Simple sentiment, text, trend and posting frequency analysis are performed on these scraped UGCs
https://github.com/kwokhing/rfacebook-demo
d3-heatmap exploratory-data-analysis r rfacebook sentiment-analysis sentiment-lexcions social-media text-analysis trend-analysis user-generated-content wordcloud
Last synced: 7 months ago
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Demo on using the Rfacebook package to scrape/mine user generated contents (UGCs) from public FB pages. Simple sentiment, text, trend and posting frequency analysis are performed on these scraped UGCs
- Host: GitHub
- URL: https://github.com/kwokhing/rfacebook-demo
- Owner: KwokHing
- License: unlicense
- Created: 2017-07-09T04:34:27.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2020-09-19T03:45:09.000Z (about 5 years ago)
- Last Synced: 2025-01-30T05:26:57.292Z (8 months ago)
- Topics: d3-heatmap, exploratory-data-analysis, r, rfacebook, sentiment-analysis, sentiment-lexcions, social-media, text-analysis, trend-analysis, user-generated-content, wordcloud
- Language: R
- Homepage:
- Size: 2.35 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
### 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__](https://cran.r-project.org/web/packages/Rfacebook/Rfacebook.pdf)
```R
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](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/](https://developers.facebook.com/)
- Replace __app_id="xxx"__ with your App ID code
- Replace __app_secret="xxx"__ with your App Secret code
```R
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__](https://www.facebook.com/thatsmyfairprice/)
```R
ntucPosts <- getPage("thatsmyfairprice", token, n = 5000, reactions = T)
```
```R
head(ntucPosts)
```
idlikes_countfrom_idfrom_namemessagecreated_timetypelinkstorycomments_countshares_countlove_counthaha_countwow_countsad_countangry_countpost_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
```R
nrow(ntucPosts)
```
2473
5) Checking the distribution of post type of this FB page
```R
table(ntucPosts$type)
```
event link note photo status video
7 375 4 1555 321 211
```R
plot(table(ntucPosts$type),ylab="Count of Post Type")
```
6) Let's take a look at the number of __Likes__ & __Reactions__ in each Post Type
```R
install.packages("sqldf")
library(sqldf)
```
```R
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")
```
```R
postType
```
typetotal_type_counttotal_sharestotal_likestotal_lovetotal_hahatotal_wowtotal_sadtotal_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 4249593014 209 1419 172 138
status 321 2016 17255 6 47 20 8 192
video 211 38630 829891593 295 392 55 65
### Get All Comments from the Scraped Facebook Posts ###
7) Loop through all the posts and get their respective comments
```R
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))
```
```R
head(ntucComments)
```
post.from_idpost.from_namepost.messagepost.created_timepost.typepost.linkpost.idpost.likes_countpost.comments_countpost.shares_count...comments.likes_countcomments.comments_countcomments.idreactions.idreactions.likes_countreactions.love_countreactions.haha_countreactions.wow_countreactions.sad_countreactions.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=3124299741408_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=3124299741408_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=3124299741408_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=3124299741408_10150856491601409 3 0 0 ... NA NA NA 124299741408_10150856491601409 3 0 0 0 0 0
```R
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
```R
names(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.from_id'
- 'comments.from_name'
- 'comments.message'
- 'comments.created_time'
- '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'
8) We will replace all __.__ with _ instead
```R
names(ntucComments) <- gsub("\\.", "_", names(ntucComments))
names(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_from_id'
- 'comments_from_name'
- 'comments_message'
- 'comments_created_time'
- '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'
9) Create a function that coverts FB date format to GMT
```R
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
```R
ntucComments$comments_datetime <- format.facebook.date(ntucComments$comments_created_time)
ntucComments$post_datetime <- format.facebook.date(ntucComments$post_created_time)
```
### Comments Variable Cleaning ###
11) Convert the comments variable column to ASCII
```R
ntucComments$comments_message <- iconv(ntucComments$comments_message, "ASCII", "UTF-8", sub="")
```
12) Remove comments made by the organisation itself
```R
ntucCommentsClean <- subset(ntucComments, comments_from_name != "NTUC FairPrice")
```
13) Substituting emoticons with text/descriptions
```R
ntucCommentsClean$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$comments_message)
```
14) Substituting line breaks, tabs, digits, url and punctuations with empty space
```R
ntucCommentsClean$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)
```R
ntucCommentsClean <- subset(ntucCommentsClean, nchar(comments_message) > 2)
```
### Lexicon-based Sentiment Analysis ###
16) load positive and negative lexicons
```R
positives = readLines("positive-words.txt")
negatives = readLines("negative-words.txt")
```
17) Adding custom abbreviation / internet slangs to lexicon
```R
positives = c(positives, 'thx', 'congrats', 'luv')
negatives = c(negatives, 'wtf', 'cancellation')
```
18) Installing required packages
```R
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
```R
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)
}
```
```R
ntucSentiScores <- score.sentiment(ntucCommentsClean$comments_message,positives,negatives,.progress = "text")
```
|======================================================================| 100%
```R
hist(ntucSentiScores$score,xlab="Sentiment Score")
```
he histogram shows that most of the comments have a score hovering around the 0 score mark. The histogram depicts a normal distribution indicating that most of the comments are neutral with some positive and negative comments
20) Now, let's add a new variable __sentiment_polar__ to determine the sentiment of the comment
```R
ntucCommentsClean$sentiment <- ntucSentiScores$score
ntucCommentsClean$sentiment_polar <- ifelse(ntucCommentsClean$sentiment == 0, "Neutral", ifelse(ntucCommentsClean$sentiment > 0, "Positive", "Negative"))
```
```R
head(ntucCommentsClean, n=2)
```
post_from_idpost_from_namepost_messagepost_created_timepost_typepost_linkpost_idpost_likes_countpost_comments_countpost_shares_count...reactions_likes_countreactions_love_countreactions_haha_countreactions_wow_countreactions_sad_countreactions_angry_countcomments_datetimepost_datetimesentimentsentiment_polar
13124299741408 NTUC FairPrice NA 2004-01-01T08:00:00+0000 photo https://www.facebook.com/thatsmyfairprice/photos/p.10150858221106409/10150858221106409/?type=3124299741408_10150858221126409 1 3 0 ... 1 0 0 0 0 0 2012-06-08 09:23:48 2004-01-01 08:00:00 0 Neutral
15124299741408 NTUC FairPrice NA 2004-01-01T08:00:00+0000 photo https://www.facebook.com/thatsmyfairprice/photos/p.10150858221106409/10150858221106409/?type=3124299741408_10150858221126409 1 3 0 ... 1 0 0 0 0 0 2015-03-04 19:44:11 2004-01-01 08:00:00 -1 Negative
```R
table(ntucCommentsClean$sentiment_polar)
```
Negative Neutral Positive
1418 8884 6986
```R
plot(table(ntucCommentsClean$sentiment_polar),ylab="Frequency")
```
```R
mean(ntucCommentsClean$sentiment)
```
0.56461129106895
mean sentiment score of 0.56 indicates a mostly neutral and positive comments
*You can continue to check the comments sentiments based on the post type as well*
*For e.g. sentiment for __post_type = "link"__*
```R
ntucLink <- subset(ntucCommentsClean, post_type == "link")
hist(ntucLink$sentiment, xlab = "Sentiment Score", main = "Sentiment Histogram of NTUC Link Posts")
table(ntucLink$sentiment_polar)
mean(ntucLink$sentiment)
plot(table(ntucLink$sentiment_polar),ylab="Frequency")
```
Negative Neutral Positive
159 908 522
0.351793580868471
### Text Frequency Analysis ###
21) Installing required packages for Text Analysis
```R
Needed = c("tm", "SnowballC", "RColorBrewer", "wordcloud")
install.packages(Needed, dependencies=TRUE)
```
Installing packages into '/home/nbcommon/R'
(as 'lib' is unspecified)
Warning message:
"dependencies 'Rcampdf', 'Rgraphviz', 'tm.lexicon.GeneralInquirer' are not available"also installing the dependency 'Rpoppler'
Warning message in install.packages(Needed, dependencies = TRUE):
"installation of package 'Rpoppler' had non-zero exit status"
22) Creating corpus, lower case transformation, removing of punctuation / numbers and stopwords
```R
library(tm)
# create corpus
corpus = Corpus(VectorSource(ntucCommentsClean$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
```R
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
```R
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)
}
```
```R
ntucPosts$timestamp <- format.facebook.date(ntucPosts$created_time)
ntucPosts$month <- format(ntucPosts$timestamp, "%Y-%m")
```
```R
df.list <- lapply(c("likes", "comments", "shares"), aggregate.matric)
df <- do.call(rbind, df.list)
```
25) Installing ggplot2 package
```R
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
```R
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
```R
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
```R
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
```R
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
```R
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
```R
time.interval <- min(ntucPosts$datetime) %--% max(ntucPosts$datetime)
sampledays <- round(time.interval / ddays(1))
ntucPostFreq <- sampledays / nrow(ntucPosts)
```
```R
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