The goal of Twitmo is to facilitate topic modeling in R with Twitter
data. Twitmo provides a broad range of methods to sample, pre-process
and visualize contents of geo-tagged tweets to make modeling the public
discourse easy and accessible.
Can I use Twitmo for pseudo-document pooling if I already sampled data earlier from Twitter without Twitmo?
Yes, this is possible in the Github version of Twitmo. You can use
pool_tweets() on any data frame, that has a ‘text’ and a ‘hashtags’
columns that are also named that way. Any additional columns you might
have, can additionally be used as document meta-data in a STM (see
below).
Twitmo has no built-in methods for this purpose, however slicing your
data time wise and fitting multiple LDA models then comparing topical
prevalence over time can be accomplished with Twitmo in conjunction
with ggplot2.
If you are using Twitmo for the first time, you might not already have
rtweet installed. If you have rtweet version >= 1.0.0 installed,
you will not be able to use certain parts of Twitmo, like
parsing/loading tweets because of breaking changes in rtweet. Since
CRAN, by default, only distributes the latest version of a package and R
does not respect upper boundaries on dependencies I am currently working
on a solution. You make sure you have the correct version of rtweet
installed by running
## install remotes package if it's not already
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_version("rtweet", version = "0.7.0", repos = "http://cran.us.r-project.org")You can install Twitmo from CRAN with:
install.packages("Twitmo")or install from Github where the correct version of rtweet will
automatically be installed.
You can install Twitmo from Github with:
## install remotes package if it's not already
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
## install dev version of Twitmo from github
remotes::install_github("abuchmueller/Twitmo")Note: Installing from Github may require you to have Rtools on your system.
Make sure you have a regular Twitter Account before start to sample your tweets.
# Live stream tweets from the UK for 30 seconds and save to "uk_tweets.json" in current working directory
get_tweets(method = 'stream',
location = "GBR",
timeout = 30,
file_name = "uk_tweets.json")
# Use your own bounding box to stream US mainland tweets
get_tweets(method = 'stream',
location = c(-125, 26, -65, 49),
timeout = 30,
file_name = "tweets_from_us_mainland.json")A small sample with raw tweets is included in the package. Access via:
raw_path <- system.file("extdata", "tweets_20191027-141233.json", package = "Twitmo")
mytweets <- load_tweets(raw_path)
#> Found 167 records... Found 193 records... Imported 193 records. Simplifying...pool <- pool_tweets(mytweets)
#>
#> 193 Tweets total
#> 158 Tweets without hashtag
#> Pooling 35 Tweets with hashtags #
#> 56 Unique hashtags total
#> Begin pooling ...Done
pool.corpus <- pool$corpus
pool.dfm <- pool$document_term_matrixfind_lda(pool.dfm)
model <- fit_lda(pool.dfm, n_topics = 7)lda_terms(model)
#> Topic.1 Topic.2 Topic.3 Topic.4 Topic.5 Topic.6 Topic.7
#> 1 bio last today paola tenrestaurant music church
#> 2 link meet laurel says crazy time today
#> 3 click people glen puppy covered birthday morning
#> 4 job first trailing downtown waffle girlhappy place
#> 5 see big oaks knoxville sooooo birthdaytasha life
#> 6 can always tuscany like life morning day
#> 7 recommend love ii us democrats early see
#> 8 anyone night design season jeff photoshoot sunday
#> 9 great fun perfectly theres sessions shamarathemodelscruggs yall
#> 10 care good sized nothing beautiful allwomen wordor which hashtags are heavily associated with each topic
lda_hashtags(model)
#> Topic
#> mood 7
#> motivate 5
#> healthcare 1
#> mrrbnsnathome 5
#> newyork 5
#> breakfast 5
#> thisismyplace 7
#> p4l 7
#> chinup 4
#> sundayfunday 4
#> saintsgameday 4
#> instapuppy 4
#> woof 4
#> tailswagging 4
#> tickfire 6
#> msiclassic 4
#> nyc 2
#> about 2
#> joethecrane 2
#> government 1
#> ladystrut19 6
#> ladystrutaccessories 6
#> smartnews 5
#> sundaythoughts 7
#> sf100 6
#> openhouse 3
#> springtx 3
#> labor 1
#> norfolk 1
#> oprylandhotel 2
#> pharmaceutical 3
#> easthanover 2
#> sales 2
#> scryingartist 5
#> beautifulskyz 5
#> knoxvilletn 4
#> downtownknoxville 4
#> heartofservice 6
#> youthmagnet 6
#> youthmentor 6
#> bonjour 4
#> trump2020 7
#> spiritchat 4
#> columbia 1
#> newcastle 6
#> oncology 1
#> nbatwitter 7
#> detroit 1Check the distribution of your LDA Model with
lda_distribution(model)
#> V1 V2 V3 V4 V5 V6 V7
#> mood 0.001 0.001 0.001 0.001 0.001 0.001 0.995
#> motivate 0.001 0.001 0.001 0.001 0.994 0.001 0.001
#> healthcare 0.996 0.001 0.001 0.001 0.001 0.001 0.001
#> mrrbnsnathome 0.002 0.002 0.002 0.002 0.989 0.002 0.002
#> newyork 0.002 0.002 0.002 0.002 0.989 0.002 0.002
#> breakfast 0.002 0.002 0.002 0.002 0.989 0.002 0.002
#> thisismyplace 0.001 0.001 0.001 0.001 0.001 0.001 0.994
#> p4l 0.001 0.001 0.001 0.001 0.001 0.001 0.994
#> chinup 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> sundayfunday 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> saintsgameday 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> instapuppy 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> woof 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> tailswagging 0.004 0.004 0.004 0.978 0.004 0.004 0.004
#> tickfire 0.001 0.001 0.001 0.001 0.001 0.996 0.001
#> msiclassic 0.001 0.001 0.001 0.994 0.001 0.001 0.001
#> nyc 0.001 0.996 0.001 0.001 0.001 0.001 0.001
#> about 0.001 0.996 0.001 0.001 0.001 0.001 0.001
#> joethecrane 0.001 0.996 0.001 0.001 0.001 0.001 0.001
#> government 0.995 0.001 0.001 0.001 0.001 0.001 0.001
#> ladystrut19 0.001 0.001 0.001 0.001 0.001 0.996 0.001
#> ladystrutaccessories 0.001 0.001 0.001 0.001 0.001 0.996 0.001
#> smartnews 0.001 0.001 0.001 0.001 0.997 0.001 0.001
#> sundaythoughts 0.000 0.000 0.000 0.000 0.000 0.000 0.997
#> sf100 0.001 0.001 0.001 0.001 0.001 0.995 0.001
#> openhouse 0.000 0.000 0.998 0.000 0.000 0.000 0.000
#> springtx 0.000 0.000 0.998 0.000 0.000 0.000 0.000
#> labor 0.995 0.001 0.001 0.001 0.001 0.001 0.001
#> norfolk 0.995 0.001 0.001 0.001 0.001 0.001 0.001
#> oprylandhotel 0.001 0.995 0.001 0.001 0.001 0.001 0.001
#> pharmaceutical 0.268 0.001 0.729 0.001 0.001 0.001 0.001
#> easthanover 0.001 0.996 0.001 0.001 0.001 0.001 0.001
#> sales 0.001 0.996 0.001 0.001 0.001 0.001 0.001
#> scryingartist 0.001 0.001 0.001 0.001 0.996 0.001 0.001
#> beautifulskyz 0.001 0.001 0.001 0.001 0.996 0.001 0.001
#> knoxvilletn 0.001 0.001 0.001 0.994 0.001 0.001 0.001
#> downtownknoxville 0.001 0.001 0.001 0.994 0.001 0.001 0.001
#> heartofservice 0.003 0.003 0.003 0.003 0.003 0.983 0.003
#> youthmagnet 0.003 0.003 0.003 0.003 0.003 0.983 0.003
#> youthmentor 0.003 0.003 0.003 0.003 0.003 0.983 0.003
#> bonjour 0.001 0.001 0.001 0.994 0.001 0.001 0.001
#> trump2020 0.001 0.001 0.001 0.001 0.001 0.001 0.994
#> spiritchat 0.001 0.001 0.001 0.996 0.001 0.001 0.001
#> columbia 0.996 0.001 0.001 0.001 0.001 0.001 0.001
#> newcastle 0.001 0.001 0.001 0.001 0.001 0.996 0.001
#> oncology 0.995 0.001 0.001 0.001 0.001 0.001 0.001
#> nbatwitter 0.000 0.000 0.000 0.000 0.000 0.000 0.997
#> detroit 0.995 0.001 0.001 0.001 0.001 0.001 0.001Sometimes you can build better topic models by blacklisting or
whitelisting certain keywords from your data. You can do this with a
keyword dictionary using the filter_tweets() function. In this example
we exclude all tweets with “football” or “mood” in them from our data.
mytweets %>% dim()
#> [1] 193 92
filter_tweets(mytweets, keywords = "football,mood", include = FALSE) %>% dim()
#> [1] 183 92Analogously if you want to run your collected tweets through a whitelist use
mytweets %>% dim()
#> [1] 193 92
filter_tweets(mytweets, keywords = "football,mood", include = TRUE) %>% dim()
#> [1] 10 92Structural topic models can be fitted with additional external covariates. In this example we metadata that comes with the tweets such as retweet count. This works with parsed unpooled tweets. Pre-processing and fitting is done with one function.
stm_model <- fit_stm(mytweets, n_topics = 7, xcov = ~ retweet_count + followers_count + reply_count + quote_count + favorite_count,
remove_punct = TRUE,
remove_url = TRUE,
remove_emojis = TRUE,
stem = TRUE,
stopwords = "en")STMs can be inspected via
summary(stm_model)
#> A topic model with 7 topics, 137 documents and a 324 word dictionary.
#> Topic 1 Top Words:
#> Highest Prob: last, today, time, love, show, week, just
#> FREX: love, show, week, give, whole, today, alway
#> Lift: alway, band, fan, give, love, miss, monster
#> Score: stop, love, last, today, week, give, show
#> Topic 2 Top Words:
#> Highest Prob: see, job, bio, link, click, can, season
#> FREX: bio, link, click, hire, latest, follow, downtown
#> Lift: bio, link, allen, area, develop, downtown, embarrass
#> Score: area, bio, link, job, click, hire, see
#> Topic 3 Top Words:
#> Highest Prob: like, one, year, want, think, read, win
#> FREX: year, way, around, parti, ppl, one, match
#> Lift: around, father, parti, ppl, pretti, start, throughout
#> Score: parti, one, year, like, way, ppl, match
#> Topic 4 Top Words:
#> Highest Prob: happen, life, right, now, say, need, hear
#> FREX: need, keep, action, support, work, right, hear
#> Lift: ’ve, action, difficult, footbal, head, keep, liter
#> Score: support, happen, say, now, need, life, action
#> Topic 5 Top Words:
#> Highest Prob: trump, know, peopl, game, best, presid, look
#> FREX: trump, know, care, got, intellig, isi, best
#> Lift: baghdadi, better, bin, fail, grow, person, sport
#> Score: isi, know, trump, care, intellig, presid, truth
#> Topic 6 Top Words:
#> Highest Prob: get, help, come, take, place, team, can
#> FREX: help, pleas, hors, colleg, communiti, made, question
#> Lift: hors, pleas, anytim, awesom, benefit, colleg, communiti
#> Score: made, help, hors, anytim, eddi, floyd, ranch
#> Topic 7 Top Words:
#> Highest Prob: will, sunday, day, live, morn, school, feel
#> FREX: day, live, morn, feel, fun, good, put
#> Lift: coffe, husband, ice, ladi, morn, park, realli
#> Score: feel, day, will, morn, live, sunday, putMake sure you have LDAvis and servr installed.
## install LDAvis package if it's not already
if (!requireNamespace("LDAvis", quietly = TRUE)) {
install.packages("LDAvis")
}
## install servr package if it's not already
if (!requireNamespace("servr", quietly = TRUE)) {
install.packages("servr")
}Export fitted models into interactive LDAvis visualizations with one
line of code
to_ldavis(model, pool.corpus, pool.dfm)
## for STM use (included in the stm package)
stm::toLDAvis(stm_model, stm_model$prep$documents)
Plot your tweets onto a static map
plot_tweets(mytweets, region = "USA(?!:Alaska|:Hawaii)", alpha=0.1)
or plot the distribution of a certain hashtag onto a static map (UK data not included)
plot_hashtag(uk_tweets, region = "UK", hashtag = "foodwaste", ignore_case=TRUE, alpha=0.2)
Use scroll wheel to zoom into and out of the map. Click markets to see
tweets. Make sure you have the leaflet package installed.
## install leaflet package if it's not already
if (!requireNamespace("leaflet", quietly = TRUE)) {
install.packages("leaflet")
}
cluster_tweets(mytweets)