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Code used in my "What's Your Twitter Story?" article on
Unless otherwise specified, code has been adapted from the book 'Text Mining with R' by Julia Silge and David Robinson.
A big thank you to both of these #rstats experts for their work in tidy text mining and for making their work so easily accessible for newcomers like myself.
Also a big thank you to Hadley Wickham for all his work on dplyr and tidyr, two packages also used in this project. His co-authored book with Garrett Grolemund called 'R for Data Science' is another exceptional resource for beginners.'R for Data Science' book link:
And finally a big (or as One Direction would say: MASSIVE) THANK YOU to Terry, and Jake without whose encouragment and support I would not have finished this. MY VERY FIRST ARTICLE!!
You can follow along with my article, and the 'Text Mining with R' explanations (, and code to get a better understanding
To download your own Twitter archive, follow the instructions here:
Save your archive folder and unzip in an easy to access place. If you have no prior experience with R or programming and need help getting started on your analysis, please give me a shout on Twitter @nazneen2411 and I will be more than happy to get you started! :)
Do note that this code works when you are subsetting(splitting) you data into two groups. I will try to get code up for just a general analysis (WITHOUT subsetting). This may not include all the graphs and plots. I will update this gist and also put a link in the article when that happens.
Import tweets.csv to R and change timestamp from chr to POSIXlt format, assign to the same dataframe (df)
#uncomment the next line and run if you do not have these packages
tweets <- read_csv("C:/Users/Nazneen/path/to/tweets.csv")
#AND TOKENIZATION WILL BE UGLY (I really have no other word to describe this)
#Otherwise as I mentioned in the article, I was ending up with partial contractions such as "couldn","don","ve"
tweets$text <- stringi::stri_trans_general(tweets$text, "latin-ascii")
#change timestamp from string to time format
tweets <- bind_rows(tweets %>%
mutate(timestamp = ymd_hms(timestamp)))
#change timezones as the above code will have all times listed in UTC
#This is not really necessary unless you're analyzing a very small amount of tweets or want the correct counts of
#tweets in a day. Which would be wrong if you're not in the UTC zone, so you need this conversion.
#Code accounts for Daylight Savings Time so no extra steps required
#I changed it to EST bc I'm in the Toronto area, replace with appropriate timezone code if necessary
tweets$timestamp <- as.POSIXlt(tweets$timestamp, tz="EST")
Plot 'tweets' to see overview of timeline and amount of tweets
ggplot(tweets, aes(x = timestamp)) +
geom_histogram(position = "identity", bins = 20, show.legend = FALSE)
Seperate/group by year, 2015 and before, and 2016 to present as two categories to see if my tweeting has changed in the 1yr2months that I was away from twitter (among other things)
The next few bits is my own code specific to how I was subsetting the data, not necessary if you're doing a general analysis.
You can skip directly to go to CONTRACTION REMOVAL. I recommend doing this for more accurate word clouds and Top words.
#change back to character otherwise this code will not work
tweets$timestamp <- as.character(tweets$timestamp)
tweets <- tweets %>%
mutate(timeperiod = case_when(grepl("2010", timestamp) ~ "past",
grepl("2011", timestamp) ~ "past",
grepl("2012", timestamp) ~ "past",
grepl("2013", timestamp) ~ "past",
grepl("2014", timestamp) ~ "past",
grepl("2015", timestamp) ~ "past",
grepl("2016", timestamp) ~ "present",
grepl("2017", timestamp) ~ "present",
grepl("2018", timestamp) ~ "present"))
Alternate code to do the above. Compact but requires greater understanding of piping and mutating. Will create a column called year which would not be created in the above code. However it can be useful depending on your needs.
Note: this code only works with POSIXlt and not POSIXct. Understand the difference between the two here:
tweets <- tweets %>%
mutate(year = (tweets$timestamp)$year + 1900) %>%
mutate(timeperiod = ifelse(year %in% 2010:2015, "past",
ifelse(year %in% 2016:2018, "present", "NA")))
Before removing stop words and subsetting into past and present, we will change all contractions.
Code adapted from:
#install.packages(c("tm", "NLP"))
#create the function, trust me you don't want to be doing this manually. It would take DAYS
fixcontractions <- function(a) {
a <- gsub("'","'", a) #the devil that are smart apostrophes being changed into normal ones
a <- gsub("won't", "will not", a)
a <- gsub("can't", "cannot", a) #expansion of can't does not have a space so I fixed that
a <- gsub("n't", " not", a)
a <- gsub("'ll"," will", a)
a <- gsub("'re", " are", a)
a <- gsub("'ve"," have", a)
a <- gsub("'m", " am", a)
a <- gsub("'d","", a) #'d can be "had" or "would"
a <- gsub("'s", "", a) #possessive noun, doesn't need to be expanded
Run the above function on 'tweets' to clean the text up.
First way:
tweets$text <- sapply(tweets$text, fixcontractions)
Second way:
#will not work if you're still using the POSIXlt timestamp, so let's change it back to character
tweets$timestamp <- as.character(tweets$timestamp)
tweets <- bind_rows(tweets %>%
mutate(text= fixcontractions(tweets$text)))
Finally subset into two dataframes based on timeperiod
twts_past <- tweets[tweets$timeperiod == "past",]
twts_present <- tweets[tweets$timeperiod == "present",]
Now to unnest, tokenize and calculate frequencies
replace_reg <- "[A-Za-z\\d]+|http://[A-Za-z\\d]+|&amp;|&lt;|&gt;|RT|https"
unnest_reg <- "([^A-Za-z_\\d#@']|'(?![A-Za-z_\\d#@]))"
#unnest and tokenize tweets to calculate frequencies and also so we can group by timeperiod
tidy_tweets <- tweets %>%
filter(!str_detect(text, "^RT")) %>%
mutate(text = str_replace_all(text, replace_reg, "")) %>%
unnest_tokens(word, text, token = "regex", pattern = unnest_reg) %>%
filter(!word %in% stop_words$word,
str_detect(word, "[a-z]"))
#Run the same code as above on twts_past and twts_present and save to tidy_past
#and tidy_present respectively
Calculate frequency of words by timeperiod (in my case)
#on full tidy_tweets, save result to another variable
freq_tweets <- tidy_tweets %>%
group_by(timeperiod) %>%
count(word, sort = TRUE) %>%
left_join(tidy_tweets %>%
group_by(timeperiod) %>%
summarise(total = n())) %>%
mutate(freq = n/total)
freq_tweets #shows the results of the code
freq_tweets <- freq_tweets %>%
select(timeperiod, word, freq) %>%
spread(timeperiod, freq) %>%
arrange(past, present)
freq_tweets #gives a nice tidy dataframe by timeperiod as a result
Plot the frequencies
ggplot(freq_tweets, aes(present, past)) +
geom_jitter(alpha = 0.1, size = 2.5, width = 0.25, height = 0.25) +
geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
scale_x_log10(labels = percent_format()) +
scale_y_log10(labels = percent_format()) +
geom_abline(color = "red")
Overall cloud
tw <- tidy_tweets %>% count(word, sort=TRUE)
tw <- tw %>% filter(n>1)
#word cloud of top 25 words
tw %>%
random.order=FALSE, rot.per=0.35,colors=brewer.pal(8, "Dark2")))
#Similarly, I ran the above code on the tidy_past and tidy_present dataframes to get the other two wordclouds
Positive and Negative words using BING lexicon for sentiment analysis
tidy_tweets %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>% = c("springgreen4", "orchid3"),
max.words = 25)
#Again, I ran the above code for both tidy_past and tidy_present to get the Top positive and negative words for those two subsets
Word Ratios - another way to see top words by subset (past/present)
word_ratios <- tidy_tweets %>%
filter(!str_detect(word, "^@")) %>% #filtering so we don't have any mentions or handles in this
count(word, timeperiod) %>%
filter(sum(n) >= 10) %>%
ungroup() %>%
spread(timeperiod, n, fill = 0) %>%
mutate_if(is.numeric, funs((. + 1) / sum(. + 1))) %>%
mutate(logratio = log(present / past)) %>%
Shows distinct words and their logratios when compared to Past and Present
word_ratios %>%
Plot top 12 words based on logratios of the two timeperiods
word_ratios %>%
group_by(logratio < 0) %>%
top_n(12, abs(logratio)) %>%
ungroup() %>%
mutate(word = reorder(word, logratio)) %>%
ggplot(aes(word, logratio, fill = logratio < 0)) +
geom_col(show.legend = FALSE) +
coord_flip() +
ylab("log odds ratio (Present/Past)") +
scale_fill_discrete(name = "", labels = c("Past", "Present"))
The End!
As I mentioned earlier, I will try to get a code up in the next few days for a very general analysis without the subsetting.
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