class: center, middle, inverse, title-slide .title[ # Automatic Sampling and Analysis of YouTube Data ] .subtitle[ ## Basic text analysis of user comments ] .author[ ### Johannes Breuer, Annika Deubel, & M. Rohangis Mohseni ] .date[ ### February 15th, 2023 ] --- layout: true <!-- START HERE WITH SLIDES --> --- ## Required Libraries for This Session ```r library(tidyverse) library(lubridate) library(tuber) library(quanteda) library(quanteda.textstats) library(wordcloud) ``` We also need two libraries that are only available from *GitHub*. You can install them using the `install_github()` function from the `remotes` package. ```r library(remotes) install_github("dill/emoGG") install_github("hadley/emo") library(emoGG) library(emo) ``` *Note*: [Emil Hvitfeldt](https://github.com/EmilHvitfeldt) has created the [`emoji` package](https://emilhvitfeldt.github.io/emoji/) which is based on the `emo` package and also available via *CRAN*. --- ## Get the Data As in the last session, we will be working with the - now processed and cleaned - comments for the [Emoji Movie Trailer](https://www.youtube.com/watch?v=r8pJt4dK_s4). In case you have collected and saved the comments before, you can just load them at this point. ```r FormattedComments <- readRDS("./data/ParsedEmojiComments.rds") ``` *Note*: Depending on where you saved the data, how you named the file, and what your current working directory is, you might have to adjust the file path. --- ## Repetition: Collecting Data If you have not collected and parsed the comments before, you, of course, need to do that before you can analyse any data. **NB**: To save time and your *YouTube* API quota limit you might not want to do this now. Step 1: Collecting the comments ```r Comments <- get_all_comments(video_id="r8pJt4dK_s4") # takes a while ``` --- ## Repetition: Parsing the Comments To run the following code the script `yt_parse.R` as well as the ones containing the helper functions (`CamelCase.R`, `ExtractEmoji.R`, and `ReplaceEmoji.R`) need to be in the working directory (you can find those files in the `scripts` folder in the workshop materials). ```r source("yt_parse.R") FormattedComments <- yt_parse(Comments) # this will take a while ``` *Note*: As an alternative to sourcing the `yt_parse.R` file you could also "manually" run the code from the slides for the session on *Processing and Cleaning User Comments* on the collected comments. --- ## Comments Over Time: Data Wrangling 🤠 For a first exploratory plot, we want to plot the development of the number of comments per week over time and show until when 50%, 75%, 90%, and 99% of the comments had been posted. This requires some data wrangling. ```r FormattedComments <- FormattedComments %>% arrange(Published) %>% mutate(date = date(Published), week = floor_date(date, unit = "week", week_start = getOption("lubridate.week.start", 1)), counter = 1) weekly_comments <- FormattedComments %>% count(week) %>% mutate(cumulative_count = cumsum(n)) PercTimes <- round(quantile(cumsum(FormattedComments$counter), probs = c(0.5, 0.75, 0.9, 0.99))) ``` # The final line computes the cumulative count quantiles (50th, 75th, 90th, and 99th percentiles) of the comments and rounds the result to the nearest integer --- ## Comments Over Time: Plot ```r weekly_comments %>% ggplot(aes(x = week, y = n)) + geom_bar(stat = "identity") + scale_x_date(expand = c(0,0)) + scale_y_continuous(expand = c(0,0), limits = c(0,10000)) + labs(title = "Number of comments over time", subtitle = "THE EMOJI MOVIE - Official Trailer (HD) \nhttps://www.youtube.com/watch?v=r8pJt4dK_s4", x = "Week", y = "# of comments") + geom_vline(xintercept = FormattedComments$week[PercTimes],linetype = "dashed", colour = "red") + geom_text(aes(x = FormattedComments$week[PercTimes][1], label = "50%", y = 3500), colour="red", angle=90, vjust = 1.2) + geom_text(aes(x = FormattedComments$week[PercTimes][2], label = "75%", y = 3500), colour="red", angle=90, vjust = 1.2) + geom_text(aes(x = FormattedComments$week[PercTimes][3], label = "90%", y = 3500), colour="red", angle=90, vjust = 1.2) + geom_text(aes(x = FormattedComments$week[PercTimes][4], label = "99%", y = 3500), colour="red", angle=90, vjust = 1.2) ``` --- ## Number of Comments Over Time: Plot <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/comments-over-time-plot-1.png" width="700px" height="500px" style="display: block; margin: auto;" /> --- ## Most Popular Comments Which comments received the highest number of likes? ```r FormattedComments %>% arrange(-LikeCount) %>% head(10) %>% select(Text, LikeCount, Published) ``` .mini[ ``` ## Text LikeCount Published ## 1 Will they show Snapchat nudes in the movie? 4287 2017-05-16 15:38:40 ## 2 The Meme Movie: Coming 2020 3260 2017-10-16 04:08:12 ## 3 Lmao the egg plant emoji never gets used? Do your research lmao 2952 2017-05-16 23:55:38 ## 4 The book is so much better because it doesn’t exist. 2883 2020-10-30 15:08:17 ## 5 I believe everyone intentionally looked this up to dislike it 1802 2020-12-23 18:32:29 ## 6 This movie reeks of board room meetings on what kids find "cool". 1690 2017-05-16 22:40:13 ## 7 The eggplant emoji never used? Suuuuuree. 1399 2017-05-17 03:10:34 ## 8 So, this thing is still a thing? Ugh, I can't really still believe that you cancelled that Popeye movie... 1281 2017-05-16 15:32:41 ## 9 This is the best part 2:38 1109 2020-06-08 18:29:03 ## 10 So apparently, the eggplant emoji is an emoji that never gets used? Shows how in touch the writers of this movie are. 1090 2021-05-02 14:35:05 ``` ] --- ## Text Mining An introduction to text mining and analysis (for social sciences) is beyond the scope of this workshop, but there are great introductions available (for free) online, e.g. - [Text Mining with R - A Tidy Approach](https://www.tidytextmining.com/) by Julia Silge & David Robinson: A tidy(verse) approach - [Tutorials for the package `quanteda`](https://tutorials.quanteda.io/) - [Text mining for humanists and social scientists in R](https://tm4ss.github.io/docs/) by Andreas Niekler & Gregor Wiedemann - [Text Mining in R](https://www.kirenz.com/post/2019-09-16-r-text-mining/) by Jan Kirenz - [Computational Text Analysis](http://theresagessler.eu/eui_cta/) by Theresa Gessler - [Automated Content Analysis](https://automatedcontentanalysis.com/) by Chung-hong Chan -- In the following, we will very briefly introduce some key terms and steps in text mining, and then go through some examples of exploring *YouTube* comments (text + emojis). --- ## Popular Text Mining Packages - [**tm**](http://tm.r-forge.r-project.org/): the first comprehensive text mining package for R - [**tidytext**](https://juliasilge.github.io/tidytext/): tidyverse tools & tidy data principles - [**quanteda**](https://quanteda.io/): very powerful text mining package with extensive documentation --- ## Text as Data (in a 🌰) **Document** = collection of text strings **Corpus** = collection of documents (+ metadata about the documents) **Token** = part of a text that is a meaningful unit of analysis (often individual words) **Vocabulary** = list of all distinct words form a corpus (i.e., all types) **Document-term matrix (DTM)** or **Document-feature matrix (DFM)** = matrix with *n* = # of documents rows and *m* = size of vocabulary columns where each cell contains the count of a particular word for a particular document --- ## Preprocessing (in a 🌰) For our examples in this session, we will go through the following preprocessing steps: **1. Basic string operations**: - Transforming to lower case - Detecting and removing certain patterns in strings (e.g., punctuation, numbers or URLs) -- **2. Tokenization**: Splitting up strings into words (could also be combinations of multiple words: n-grams) -- **3. Stopword removal**: Stopwords are very frequent words that appear in almost all texts (e.g., "a","but","it", "the") but have low informational value for most analyses (at least in the social sciences) --- ## Preprocessing (in a 🌰) **NB**: - There are many other preprocessing options that we will not use for our examples, such as [stemming](https://en.wikipedia.org/wiki/Stemming), [lemmatization](https://en.wikipedia.org/wiki/Lemmatisation) or natural language processing pipelines (e.g., to detect and select specific word types, such as nouns and adjectives). - Keep in mind that the choice and order of these preprocessing steps is important and should be informed by your research question. --- ## Tokenization Before we tokenize the comments, we want to remove newline commands from the strings. ```r FormattedComments <- FormattedComments %>% mutate(TextEmojiDeleted = str_replace_all(TextEmojiDeleted, pattern = "\\\n", replacement = " ")) ``` --- ## Tokenization Now we can tokenize the comments and remove punctuation, symbols, numbers, and URLs. ```r toks <- FormattedComments %>% pull(TextEmojiDeleted) %>% char_tolower() %>% tokens(remove_numbers = TRUE, remove_punct = TRUE, remove_separators = TRUE, remove_symbols = TRUE, split_hyphens = TRUE, remove_url = TRUE) ``` --- ## Document-Feature Matrix With the tokens we can create a [document-feature matrix](https://quanteda.io/reference/dfm.html) (DFM) and remove [stopwords](https://en.wikipedia.org/wiki/Stop_word). ```r commentsDfm <- dfm(toks, remove = quanteda::stopwords("english")) ``` --- ## Most Frequent Words ```r TermFreq <- textstat_frequency(commentsDfm) head(TermFreq, n = 20) ``` ``` ## feature frequency rank docfreq group ## 1 movie 11706 1 8876 all ## 2 emoji 3191 2 2759 all ## 3 like 2816 3 2427 all ## 4 just 2486 4 2199 all ## 5 nom 2239 5 1 all ## 6 people 1541 6 1332 all ## 7 sony 1508 7 1397 all ## 8 bad 1395 8 1275 all ## 9 good 1312 9 1198 all ## 10 one 1227 10 1111 all ## 11 hate 1127 11 1019 all ## 12 emojis 1099 12 990 all ## 13 see 1047 13 927 all ## 14 watch 1030 14 948 all ## 15 make 1010 15 908 all ## 16 think 992 16 898 all ## 17 know 960 17 879 all ## 18 popeye 939 18 862 all ## 19 dislikes 912 19 895 all ## 20 can 886 20 772 all ``` --- ## Removing Tokens We may want to remove additional words (that are not included in the stopwords list) if we consider them irrelevant for our analyses. ```r custom_stopwords <- c("nom", "just", "one") commentsDfm <- dfm(toks, remove = c(quanteda::stopwords("english"), custom_stopwords)) TermFreq <- textstat_frequency(commentsDfm) ``` For more options for selecting or removing tokens, see the [quanteda documentation](https://tutorials.quanteda.io/basic-operations/tokens/tokens_select/). --- ## Wordclouds ```r wordcloud(words = TermFreq$feature, freq = TermFreq$frequency, min.freq = 10, max.words = 50, random.order = FALSE, rot.per = 0.35, colors = brewer.pal(8, "Dark2")) ``` *Note*: You can adjust what is plotted by, e.g., changing the minimum frequency (`min.freq`) or the maximum # of words (`max.words`). Check `?wordcloud` for more customization options. --- ## Wordclouds <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/cloudy-plot-1.png" width="700px" height="700px" style="display: block; margin: auto;" /> --- ## Plot Most Frequent Words ```r TermFreq %>% head(n = 20) %>% ggplot(aes(x = reorder(feature, frequency), y = frequency)) + geom_bar(stat="identity") + labs(title = "Most frequent words in comments", subtitle = "THE EMOJI MOVIE - Official Trailer (HD) \nhttps://www.youtube.com/watch?v=r8pJt4dK_s4", x = "", y = "Frequency") + scale_y_continuous(expand = c(0,0), limits = c(0,12000)) + coord_flip() ``` --- ## Plot Most Frequent Words <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/word-freq-plot-1.png" width="650px" height="490px" style="display: block; margin: auto;" /> --- ## Plot Docfreq Instead of the raw frequency of words we can also look at the number of comments that a particular word appears in. This metric takes into account that words might be used multiple times in the same comment. ```r TermFreq %>% head(n = 20) %>% ggplot(aes(x = reorder(feature, docfreq), y = docfreq)) + geom_bar(stat="identity") + labs(title = "Words that appear in the highest number of comments", subtitle = "THE EMOJI MOVIE - Official Trailer (HD) \nhttps://www.youtube.com/watch?v=r8pJt4dK_s4", x = "", y = "# of comments") + scale_y_continuous(expand = c(0,0), limits = c(0,10000)) + coord_flip() ``` --- ## Plot Docfreq <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/docfreq-plot-1.png" width="650px" height="490px" style="display: block; margin: auto;" /> --- ## Emojis In most of the research studying user-generated text from social media, emojis have, so far, been largely ignored. However, emojis convey emotions and meaning, and can, thus, provide additional information or context when working with textual data. -- In the following, we will do some exploratory analysis of emoji frequencies in *YouTube* comments. Before we can start, we first need to do some data cleaning again, then tokenize the emojis as some comments include more than one emoji, and create an emoji DFM. ```r emoji_toks <- FormattedComments %>% mutate(Emoji = na_if(Emoji, "NA")) %>% # define missings mutate (Emoji = str_trim(Emoji)) %>% # remove spaces filter(!is.na(Emoji)) %>% # only keep comments with emojis pull(Emoji) %>% # pull out column cotaining emoji labels tokens(what = "fastestword") # tokenize emoji labels EmojiDfm <- dfm(emoji_toks) # create DFM for emojis ``` --- ## Most Frequent Emojis ```r EmojiFreq <- textstat_frequency(EmojiDfm) head(EmojiFreq, n = 10) ``` ``` ## feature frequency rank docfreq group ## 1 emoji_pileofpoo 3999 1 536 all ## 2 emoji_eggplant 3578 2 279 all ## 3 emoji_facewithtearsofjoy 2873 3 853 all ## 4 emoji_unamusedface 2488 4 677 all ## 5 emoji_bbutton(bloodtype) 1878 5 130 all ## 6 emoji_middlefinger 1866 6 298 all ## 7 emoji_grinningface 1562 7 374 all ## 8 emoji_flushedface 1239 8 256 all ## 9 emoji_thumbsdown 1157 9 264 all ## 10 emoji_facewithsymbolsonmouth 982 10 104 all ``` --- ## Plot Most Frequent Emojis ```r EmojiFreq %>% head(n = 10) %>% ggplot(aes(x = reorder(feature, frequency), y = frequency)) + geom_bar(stat="identity") + labs(title = "Most frequent emojis in comments", subtitle = "THE EMOJI MOVIE - Official Trailer (HD) \nhttps://www.youtube.com/watch?v=r8pJt4dK_s4", x = "", y = "Frequency") + scale_y_continuous(expand = c(0,0), limits = c(0,5000)) + coord_flip() ``` *Note*: Similar to what we did for the comment text before we could replace `frequency` with `docfreq` in the above code to create a plot with the emojis that appear in the highest number of comments. --- ## Plot Most Frequent Emojis <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/emoji-barplot-1.png" width="700px" height="500px" style="display: block; margin: auto;" /> --- ## Emoji Frequency Plot: Preparation (1) The previous emoji frequency plot was a bit 😪. To make things prettier, we can use the actual emojis instead of the text labels in our plot. Doing this takes a bit of preparation.¹ As a first step, we need an emoji lookup table in which the values in the name column have the same format as the labels in the feature column of our `EmojiFreq` object. ```r emoji_lookup <- jis %>% select(runes, name) %>% mutate(runes = str_to_lower(runes), name = str_to_lower(name)) %>% mutate(name = str_replace_all(name, " ", "")) %>% mutate(name = paste0("emoji_", name)) ``` ¹For an alternative approach to using emojis in `ggplot2` see this [blog post by Emil Hvitfeldt](https://www.emilhvitfeldt.com/post/2020-01-02-real-emojis-in-ggplot2/). --- ## Emoji Frequency Plot: Preparation (2) The second step of preparation for the nicer emoji frequency plot is creating mappings of emojis to data points so that we can use emojis instead of points in a scatter plot.¹ ```r top_emojis <- 1:10 for(i in top_emojis){ name <- paste0("mapping", i) assign(name, do.call(geom_emoji,list(data = EmojiFreq[i,], emoji = gsub("^0{2}","", strsplit(tolower(emoji_lookup$runes[emoji_lookup$name == as.character(EmojiFreq[i,]$feature)])," ")[[1]][1])))) } ``` ¹ Please note: this code has not been tested systematically. Depending on which emojis are most frequent for a video, this might not work because (a) one of the emojis is not included in the emoji lookup table (which uses the `jis` data frame from the [`emo` package](https://github.com/hadley/emo)) or (b) the content in the `runes` column doesn't match the format/code that the `emoji` argument in the `geom_emoji` function from the [`emoGG` package](https://github.com/dill/emoGG) expects. --- ## 😎 Emoji Frequency Plot .small[ ```r EmojiFreq %>% head(n = 10) %>% ggplot(aes(x = reorder(feature, -frequency), y = frequency)) + geom_bar(stat="identity", color = "black", fill = "#FF74A6", alpha = 0.7) + geom_point() + labs(title = "Most frequent emojis in comments", subtitle = "THE EMOJI MOVIE - Official Trailer (HD) \nhttps://www.youtube.com/watch?v=r8pJt4dK_s4", x = "", y = "Frequency") + scale_y_continuous(expand = c(0,0), limits = c(0,5000)) + theme(panel.grid.major.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank()) + mapping1 + mapping2 + mapping3 + mapping4 + mapping5 + mapping6 + mapping7 + mapping8 + mapping9 + mapping10 ``` ] --- ## 😎 Emoji Frequency Plot <img src="data:image/png;base64,#B1_Basic_text_analysis_of_user_comments_files/figure-html/cool-emoji-plot-1.png" width="650px" height="500px" style="display: block; margin: auto;" /> --- class: center, middle ## [Exercise](https://jobreu.github.io/youtube-workshop-gesis-2023/exercises/Exercise_B1_Basic_text_analysis_of_user_comments.html) time 🏋️‍♀️💪🏃🚴 ## [Solutions](https://jobreu.github.io/youtube-workshop-gesis-2023/solutions/Exercise_B1_Basic_text_analysis_of_user_comments.html)