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Commencement Example

Source: vignettes/commencement_example.Rmd
commencement_example.Rmd

Built with R 4.5.0

This example uses the Receptiviti API to analyze commencement speeches.

Data

We’ll start by collecting and processing the speeches.

Collection

The speeches used to be provided more directly, but the service hosting them has since shut down.

They are still available in a slightly less convenient form, as the source of a site that displays them: whatrocks.github.io/commencement-db.

First, we can retrieve metadata from a separate repository:

speeches <- read.csv(paste0(
  "https://raw.githubusercontent.com/whatrocks/markov-commencement-speech",
  "/refs/heads/master/speech_metadata.csv"
))

library(knitr)
kable(speeches[1:5, 2:4])
name school year
Aaron Sorkin Syracuse University 2012
Abigail Washburn Colorado College 2012
Adam Savage Sarah Lawrence College 2012
Adrienne Rich Douglass College 1977
Ahmed Zewail Caltech 2011

One file in the source repository contains an invalid character on Windows (:), so we’ll need to pull them in individually, rather than cloning the repository:

text_dir <- "../../commencement_speeches/"
dir.create(text_dir, FALSE)

text_url <- paste0(
  "https://raw.githubusercontent.com/whatrocks/commencement-db",
  "/refs/heads/master/src/pages/"
)
for (file in speeches$filename) {
  out_file <- paste0(text_dir, sub(":", "", file, fixed = TRUE))
  if (!file.exists(out_file)) {
    text <- readLines(paste0(
      text_url, sub(".txt", "/index.md", file, fixed = TRUE)
    ), warn = FALSE)
    writeLines(
      text[-seq_len(max(grep("^---", text)) + 1)],
      out_file
    )
  }
}

Text Preparation

Now we can read in the texts and associate them with their metadata:

speeches$text <- vapply(speeches$filename, function(file) {
  paste(readLines(paste0(
    text_dir, sub(":", "", file, fixed = TRUE)
  ), warn = FALSE), collapse = " ")
}, "")

The texts contain a few special characters, which we can replace:

# general special character replacement
library(lingmatch)
clean <- lma_dict(special, as.function = gsub)
speeches$text <- clean(speeches$text)

# ensure list items are treated as separate sentences
speeches$text <- gsub("([a-z]) •", "\\1. ", speeches$text)

Load Package

If this is your first time using the package, see the Get Started guide to install it and set up your API credentials.

Analyze Full Texts

We might start by seeing if any speeches stand out in terms of language style, or if there are any trends in content over time.

Full: Process Text

Now we can send the texts to the API for scoring, and join the results we get to the metadata:

# since our texts are from speeches,
# it might make sense to use the spoken norming context
processed <- receptiviti(speeches$text, version = "v2", context = "spoken")
processed <- cbind(speeches[, 1:4], processed)

kable(processed[1:5, -(1:7)], digits = 3, row.names = FALSE)
summary.words_per_sentence summary.sentence_count summary.six_plus_words summary.capitals summary.emojis summary.emoticons summary.hashtags summary.urls big_5.extraversion big_5.active big_5.assertive big_5.cheerful big_5.energetic big_5.friendly big_5.sociable big_5.openness big_5.adventurous big_5.artistic big_5.emotionally_aware big_5.imaginative big_5.intellectual big_5.liberal big_5.conscientiousness big_5.ambitious big_5.cautious big_5.disciplined big_5.dutiful big_5.organized big_5.self_assured big_5.neuroticism big_5.aggressive big_5.anxiety_prone big_5.impulsive big_5.melancholy big_5.self_conscious big_5.stress_prone big_5.agreeableness big_5.cooperative big_5.empathetic big_5.genuine big_5.generous big_5.humble big_5.trusting social_dynamics.social social_dynamics.affiliation social_dynamics.inward_focus social_dynamics.outward_focus social_dynamics.authentic social_dynamics.negations social_dynamics.clout drives.affiliation drives.achievement drives.risk_seeking drives.risk_aversion drives.risk_focus drives.power drives.reward cognition.analytical_thinking cognition.cognitive_processes cognition.causation cognition.certainty cognition.comparisons cognition.differentiation cognition.discrepancies cognition.insight cognition.tentative temporal_and_orientation.focus_past temporal_and_orientation.focus_present temporal_and_orientation.focus_future temporal_and_orientation.self_focus temporal_and_orientation.external_focus sallee.sentiment sallee.goodfeel sallee.badfeel sallee.emotionality sallee.non_emotion sallee.ambifeel sallee.admiration sallee.amusement sallee.excitement sallee.gratitude sallee.joy sallee.love sallee.anger sallee.boredom sallee.disgust sallee.fear sallee.sadness sallee.calmness sallee.curiosity sallee.surprise sparse_sallee.sentiment sparse_sallee.goodfeel sparse_sallee.badfeel sparse_sallee.emotionality sparse_sallee.non_emotion sparse_sallee.ambifeel sparse_sallee.admiration sparse_sallee.amusement sparse_sallee.excitement sparse_sallee.gratitude sparse_sallee.joy sparse_sallee.love sparse_sallee.anger sparse_sallee.boredom sparse_sallee.disgust sparse_sallee.fear sparse_sallee.sadness sparse_sallee.calmness sparse_sallee.curiosity sparse_sallee.surprise liwc15.analytical_thinking liwc15.clout liwc15.authentic liwc15.emotional_tone liwc15.six_plus_words liwc15.dictionary_words liwc15.function_words liwc15.pronouns liwc15.personal_pronouns liwc15.i liwc15.we liwc15.you liwc15.she_he liwc15.they liwc15.impersonal_pronouns liwc15.articles liwc15.prepositions liwc15.auxiliary_verbs liwc15.adverbs liwc15.conjunctions liwc15.negations liwc15.other_grammar liwc15.verbs liwc15.adjectives liwc15.comparisons liwc15.interrogatives liwc15.numbers liwc15.quantifiers liwc15.affective_processes liwc15.positive_emotion_words liwc15.negative_emotion_words liwc15.anxiety_words liwc15.anger_words liwc15.sad_words liwc15.social_processes liwc15.family liwc15.friends liwc15.female liwc15.male liwc15.cognitive_processes liwc15.insight liwc15.causation liwc15.discrepancies liwc15.tentative liwc15.certainty liwc15.differentiation liwc15.perceptual_processes liwc15.see liwc15.hear liwc15.feel liwc15.biological_processes liwc15.body liwc15.health liwc15.sexual liwc15.ingestion liwc15.drives liwc15.affiliation liwc15.achievement liwc15.power liwc15.reward liwc15.risk liwc15.time_orientation liwc15.focus_past liwc15.focus_present liwc15.focus_future liwc15.relativity liwc15.motion liwc15.space liwc15.time liwc15.personal_concerns liwc15.work liwc15.leisure liwc15.home liwc15.money liwc15.religion liwc15.death liwc15.informal_language liwc15.swear_words liwc15.netspeak liwc15.assent liwc15.nonfluencies liwc15.filler_words liwc15.all_punctuation liwc15.periods liwc15.commas liwc15.colons liwc15.semicolons liwc15.question_marks liwc15.exclamations liwc15.dashes liwc15.quotes liwc15.apostrophes liwc15.parentheses liwc15.other_punctuation disc_dimensions.bold_assertive_outgoing disc_dimensions.calm_methodical_reserved disc_dimensions.people_relationship_emotion_focus disc_dimensions.task_system_object_focus disc_dimensions.d_axis disc_dimensions.i_axis disc_dimensions.s_axis disc_dimensions.c_axis disc_dimensions.d_axis_proportional disc_dimensions.i_axis_proportional disc_dimensions.s_axis_proportional disc_dimensions.c_axis_proportional
19.522 134 0.192 0.030 0 0 0.000 0 64.363 58.626 58.353 49.845 71.487 58.030 68.059 50.421 49.369 52.583 66.630 67.808 35.576 44.799 61.610 53.635 60.639 47.382 71.734 56.499 48.378 59.538 65.811 52.359 37.228 61.668 42.196 50.852 59.861 41.189 48.178 72.834 62.701 47.853 53.700 60.770 45.872 55.720 57.613 59.304 45.660 58.078 45.872 49.981 63.550 55.012 55.934 46.702 50.347 50.226 38.625 49.536 44.840 48.413 38.166 46.326 45.965 35.126 0.060 0.092 0.021 0.045 0.072 0.028 0.113 0.085 0.226 0.774 0.028 0.036 0.008 0.014 0.018 0.017 0.034 0.044 0.014 0.018 0.024 0.024 0.007 0.005 0.000 -0.012 0.022 0.034 0.060 0.940 0.003 0.005 0.002 0.003 0.005 0.003 0.007 0.015 0.013 0.013 0.011 0.000 0.000 0.002 0 0.510 0.811 0.575 0.590 0.192 0.924 0.544 0.172 0.117 0.045 0.010 0.041 0.010 0.011 0.055 0.073 0.123 0.097 0.048 0.072 0.013 0.281 0.185 0.037 0.024 0.021 0.024 0.021 0.048 0.033 0.015 0.003 0.006 0.003 0.123 0.006 0.005 0.006 0.011 0.097 0.024 0.016 0.014 0.017 0.012 0.023 0.021 0.008 0.007 0.005 0.019 0.001 0.013 0.002 0.002 0.073 0.023 0.016 0.021 0.017 0.006 0.173 0.060 0.092 0.021 0.153 0.023 0.063 0.070 0.070 0.039 0.018 0.005 0.004 0.002 0.003 0.006 0.004 0.000 0.000 0.002 0.000 0.173 0.050 0.063 0.002 0.002 0.001 0.000 0.008 0.012 0.032 0.000 0.002 56.553 43.447 68.786 48.847 52.559 62.371 54.667 46.068 0.244 0.289 0.253 0.214
28.587 109 0.246 0.032 0 0 0.001 0 55.126 61.659 58.563 43.088 54.867 52.055 57.030 71.208 38.108 68.145 61.884 85.177 52.581 61.593 54.866 44.958 40.177 45.873 66.678 57.343 56.188 54.300 64.079 47.047 36.783 57.605 45.127 37.823 57.667 43.387 63.945 64.433 58.485 38.083 48.395 45.630 44.943 66.760 36.342 75.662 37.239 44.081 44.943 59.008 40.474 52.392 45.254 53.154 43.940 64.268 34.532 45.165 40.419 39.171 32.023 38.154 46.692 36.935 0.042 0.085 0.013 0.066 0.039 0.033 0.135 0.102 0.280 0.720 0.047 0.048 0.011 0.012 0.022 0.011 0.023 0.035 0.006 0.009 0.042 0.028 0.019 0.017 0.003 0.023 0.059 0.035 0.108 0.892 0.014 0.025 0.000 0.004 0.010 0.000 0.009 0.008 0.000 0.000 0.026 0.000 0.011 0.013 0 0.753 0.598 0.829 0.710 0.246 0.890 0.520 0.152 0.104 0.066 0.008 0.022 0.005 0.004 0.047 0.076 0.145 0.064 0.046 0.065 0.008 0.230 0.153 0.040 0.016 0.014 0.013 0.010 0.054 0.039 0.015 0.006 0.003 0.003 0.089 0.001 0.003 0.006 0.003 0.088 0.025 0.014 0.009 0.019 0.010 0.018 0.042 0.015 0.020 0.006 0.020 0.008 0.006 0.000 0.004 0.075 0.022 0.022 0.026 0.012 0.003 0.140 0.042 0.085 0.013 0.173 0.021 0.087 0.068 0.069 0.031 0.025 0.004 0.005 0.002 0.003 0.010 0.002 0.005 0.001 0.001 0.001 0.136 0.033 0.040 0.001 0.000 0.002 0.000 0.018 0.008 0.024 0.001 0.008 52.065 47.935 58.657 40.456 45.895 55.262 53.026 44.037 0.232 0.279 0.268 0.222
12.072 125 0.221 0.034 0 0 0.000 0 58.409 51.115 63.655 51.542 52.596 52.497 57.839 56.465 47.289 51.411 58.862 57.768 51.601 50.602 60.010 62.166 48.519 56.739 58.906 64.698 39.727 61.179 61.789 58.539 53.385 59.776 43.797 55.961 52.055 38.610 50.719 55.634 54.418 34.803 62.363 56.194 38.739 55.696 55.097 62.863 53.799 56.599 38.739 62.322 60.753 53.448 53.238 48.850 49.997 44.902 50.653 49.244 69.531 43.381 41.069 45.942 57.196 40.690 0.038 0.129 0.019 0.045 0.068 0.056 0.130 0.073 0.228 0.772 0.031 0.063 0.001 0.004 0.012 0.016 0.031 0.017 0.002 0.009 0.023 0.020 0.008 0.011 0.001 0.025 0.041 0.016 0.059 0.941 0.004 0.024 0.000 0.014 0.001 0.006 0.004 0.007 0.001 0.001 0.000 0.003 0.000 0.000 0 0.418 0.789 0.630 0.729 0.221 0.915 0.564 0.195 0.113 0.045 0.002 0.057 0.004 0.005 0.082 0.060 0.133 0.100 0.058 0.059 0.019 0.299 0.186 0.047 0.020 0.028 0.018 0.025 0.054 0.039 0.014 0.003 0.002 0.004 0.113 0.004 0.001 0.001 0.006 0.125 0.034 0.016 0.014 0.023 0.023 0.026 0.017 0.005 0.005 0.007 0.009 0.003 0.003 0.000 0.001 0.072 0.012 0.025 0.023 0.017 0.005 0.187 0.038 0.129 0.019 0.143 0.018 0.072 0.058 0.059 0.046 0.007 0.001 0.009 0.000 0.000 0.006 0.002 0.000 0.001 0.002 0.000 0.184 0.076 0.052 0.007 0.000 0.006 0.001 0.003 0.004 0.033 0.002 0.001 62.761 37.239 62.708 52.666 57.492 62.734 48.323 44.286 0.270 0.295 0.227 0.208
32.390 59 0.312 0.012 0 0 0.000 0 59.149 29.627 71.979 45.060 65.171 61.465 47.947 68.660 57.135 53.055 53.713 42.775 72.075 56.649 42.993 36.713 52.205 38.087 61.052 33.360 49.481 57.400 61.881 49.823 50.881 57.820 45.523 46.527 43.515 46.242 26.834 32.728 55.522 50.803 48.968 67.605 51.356 36.110 48.683 36.827 41.080 64.000 51.356 49.287 46.700 64.254 53.011 73.809 40.819 66.948 49.473 36.675 52.648 51.428 51.055 40.996 64.101 42.843 0.015 0.102 0.006 0.008 0.058 0.007 0.110 0.102 0.216 0.784 0.010 0.041 0.000 0.008 0.016 0.016 0.038 0.031 0.005 0.024 0.033 0.028 0.004 0.003 0.001 0.002 0.022 0.020 0.041 0.959 0.000 0.016 0.000 0.001 0.001 0.005 0.000 0.000 0.000 0.003 0.003 0.004 0.000 0.000 0 0.799 0.902 0.226 0.460 0.312 0.884 0.514 0.130 0.066 0.008 0.018 0.026 0.006 0.008 0.064 0.064 0.159 0.065 0.041 0.068 0.010 0.218 0.123 0.049 0.027 0.016 0.010 0.029 0.047 0.029 0.018 0.004 0.004 0.004 0.139 0.003 0.002 0.029 0.013 0.122 0.040 0.009 0.011 0.025 0.016 0.035 0.018 0.005 0.006 0.003 0.017 0.003 0.009 0.003 0.001 0.101 0.031 0.015 0.044 0.010 0.005 0.123 0.015 0.102 0.006 0.099 0.011 0.061 0.028 0.075 0.066 0.003 0.001 0.006 0.001 0.002 0.001 0.000 0.000 0.000 0.000 0.001 0.166 0.030 0.072 0.009 0.005 0.001 0.000 0.015 0.019 0.012 0.002 0.001 47.897 52.103 68.591 45.066 46.460 57.317 59.782 48.457 0.219 0.270 0.282 0.229
24.327 101 0.326 0.024 0 0 0.000 0 71.968 50.677 66.095 71.805 65.531 64.312 55.217 68.608 73.095 48.610 47.741 76.015 64.931 43.375 72.554 69.183 47.031 54.417 70.859 51.675 67.484 32.746 50.272 42.742 29.499 34.411 33.995 29.642 62.054 54.525 37.826 52.394 70.989 38.718 67.106 45.697 45.252 44.242 37.979 56.231 45.742 52.428 45.252 67.293 58.329 41.322 46.470 63.494 55.802 70.738 43.410 53.916 41.123 45.683 43.173 43.746 52.472 35.439 0.023 0.079 0.021 0.024 0.041 0.130 0.176 0.046 0.246 0.754 0.026 0.068 0.000 0.022 0.025 0.033 0.037 0.013 0.000 0.006 0.020 0.012 0.011 0.010 0.000 0.050 0.061 0.011 0.080 0.920 0.008 0.025 0.000 0.012 0.003 0.007 0.011 0.000 0.000 0.000 0.003 0.006 0.008 0.006 0 0.865 0.725 0.527 0.937 0.326 0.867 0.516 0.106 0.065 0.024 0.009 0.027 0.002 0.003 0.042 0.085 0.154 0.071 0.036 0.068 0.013 0.195 0.109 0.047 0.022 0.012 0.015 0.014 0.057 0.050 0.006 0.002 0.002 0.001 0.089 0.004 0.002 0.000 0.003 0.108 0.030 0.018 0.013 0.018 0.011 0.028 0.013 0.004 0.004 0.003 0.011 0.003 0.004 0.002 0.002 0.090 0.022 0.028 0.035 0.021 0.003 0.122 0.023 0.079 0.021 0.148 0.015 0.079 0.056 0.073 0.058 0.007 0.003 0.009 0.001 0.000 0.002 0.000 0.001 0.001 0.000 0.000 0.140 0.043 0.066 0.004 0.002 0.001 0.000 0.011 0.003 0.006 0.001 0.002 70.956 29.044 61.241 55.539 62.776 65.920 42.175 40.163 0.297 0.312 0.200 0.190

Full: Analyze Style

To get at stylistic uniqueness, we can calculate Language Style Matching between each speech and the mean of all speeches:

# Currently, the lingmatch package only automatically handles
# `liwc.` prefixes, so we'll rename the `liwc15.` ones.
colnames(processed) <- sub("liwc15", "liwc", colnames(processed), fixed = TRUE)

processed$lsm_mean <- lingmatch(processed, mean, type = "lsm")$sim

kable(processed[
  order(processed$lsm_mean)[1:10],
  c("name", "school", "year", "lsm_mean", "summary.word_count")
], digits = 3, row.names = FALSE)
name school year lsm_mean summary.word_count
Gary Malkowski Gallaudet University 2011 0.751 2154
Theodor ‘Dr. Seuss’ Geisel Lake Forest College 1977 0.764 96
Makoto Fujimura Belhaven University 2011 0.820 2569
Dwight Eisenhower Penn State 1955 0.831 2518
George C. Marshall Harvard University 1947 0.833 1449
Lewis Lapham St. John’s College 2003 0.835 3688
Rev. Joseph L. Levesque Niagara University 2007 0.846 483
Edward W. Brooke Wellesley College 1969 0.847 3083
Janet Napolitano Northeastern University 2014 0.853 1526
Whoopi Goldberg Savannah College of Art and Design 2011 0.855 1248

Here, it is notable that the most stylistically unique speech was delivered in American Sign Language, and the second most stylistically unique speech was a short rhyme.

We might also want to see which speeches are most similar to one another:

# calculate all pairwise comparisons
lsm_pairs <- lingmatch(processed, type = "lsm", symmetrical = TRUE)$sim

# set self-matches to 0
diag(lsm_pairs) <- 0

# identify the closest match to each speech
speeches$match <- max.col(lsm_pairs, "last")
best_match <- lsm_pairs[
  speeches$match + (seq_len(nrow(lsm_pairs)) - 1) * nrow(lsm_pairs)
]

# look at the top matches
top_matches <- order(-best_match)[1:20]
top_matches <- data.frame(a = top_matches, b = speeches$match[top_matches])
top_matches <- top_matches[!duplicated(apply(
  top_matches, 1, function(pair) paste(sort(pair), collapse = "")
)), ]
kable(data.frame(
  speeches[top_matches$a, 2:4],
  Similarity = best_match[top_matches$a],
  speeches[top_matches$b, 2:4],
  check.names = FALSE
), digits = 3, row.names = FALSE)
name school year Similarity name school year
Cynthia Enloe Connecticut College 2011 0.999 Howard Gordon Connecticut College 2013
Benjamin Carson Jr. Niagara University 2003 0.984 Jonathon Youshaei Deerfield High School 2009
John Legend University of Pennsylvania 2014 0.981 Sheryl Sandberg City Colleges of Chicago 2014
Arianna Huffington Sarah Lawrence College 2011 0.980 Ronald Reagan Eureka College 1957
Amy Poehler Harvard University 2011 0.978 Sheryl Sandberg City Colleges of Chicago 2014
Drew Houston Massachusetts Institute of Technology 2013 0.976 Melissa Harris-Perry Wellesley College 2012
Alan Alda Connecticut College 1980 0.975 Nora Ephron Wellesley College 1996
Arianna Huffington Vassar College 2015 0.975 Tim Cook Auburn University 2010
James Carville Hobart and William Smith Colleges 2013 0.975 Woody Hayes Ohio State University 1986
Mindy Kaling Harvard Law School 2014 0.975 Stephen Colbert Wake Forest University 2015
Barbara Bush Wellesley College 1990 0.975 Daniel S. Goldin Massachusetts Institute of Technology 2001

Full: Analyze Content

To look at content over time, we might focus on a potentially interesting framework, such as drives:

library(splot)

# select drives categories
drives <- processed[, grep("drives", colnames(processed), value = TRUE, fixed = TRUE)]

# identify those more correlated with time (excluding the few earliest cases)
later_times <- processed$year > 1980
drives <- drives[, order(-abs(cor(
  drives[later_times, ], processed$year[later_times]
)))[1:3]]

splot(
  drives ~ year, processed, year > 1980,
  title = FALSE, laby = "Score", lpos = "top"
)

drives categories over time

To better visualize the effects, we might look between aggregated blocks of time:

time_median <- median(processed$year)
processed$time_period <- paste(c("<", ">="), time_median)[
  (processed$year >= time_median) + 1
]

splot(
  scale(drives) ~ time_period, processed,
  title = FALSE, laby = "Score (Scaled)", lpos = "top"
)

drives categories between time periods

This suggests that references to risk and reward have increased since the 2000s while references to power have decreased at a similar rate. (Note that error bars represent how much variance there is within groups, which allows you to eyeball the statistical significance of mean differences.)

The shift in emphasis from power to risk-reward could reflect that commencement speakers are now focusing more abstractly on the potential benefits and hazards of life after graduation, whereas earlier speakers more narrowly focused on ambition and dominance (perhaps referring to power held by past alumni and projecting the potential for graduates to climb social ladders in the future). You could examine a sample of speeches that show this pattern most dramatically (speeches high in risk-reward and low in power in recent years, and vice versa for pre-2009 speeches) to help determine how these themes have shifted and what specific motives or framing devices seem to have been (de)emphasized.

Analyze Segments

Another thing we might look for is trends within each speech. For instance, are there common emotional trajectories over the course of a speech?

One way to look at this would be to split texts into roughly equal sizes, and score each section:

# split texts into 3 segments each, keeping sentences together
segmented_text <- read.segments(
  text = speeches$text, segment = 3, bysentence = TRUE
)
segmented_text <- cbind(speeches[segmented_text$input, 1:4], segmented_text)

kable(segmented_text[1:9, 2:7], row.names = FALSE)
name school year input segment WC
Aaron Sorkin Syracuse University 2012 1 1 853
Aaron Sorkin Syracuse University 2012 1 2 842
Aaron Sorkin Syracuse University 2012 1 3 830
Abigail Washburn Colorado College 2012 2 1 1008
Abigail Washburn Colorado College 2012 2 2 1024
Abigail Washburn Colorado College 2012 2 3 969
Adam Savage Sarah Lawrence College 2012 3 1 489
Adam Savage Sarah Lawrence College 2012 3 2 488
Adam Savage Sarah Lawrence College 2012 3 3 477

Segments: Process Text

Now we can send each segment to the API to be scored:

processed_segments <- receptiviti(
  segmented_text$text,
  version = "v2", context = "spoken"
)
segmented_text <- cbind(segmented_text, processed_segments)

kable(segmented_text[1:9, -(1:10)], digits = 3, row.names = FALSE)
summary.word_count summary.words_per_sentence summary.sentence_count summary.six_plus_words summary.capitals summary.emojis summary.emoticons summary.hashtags summary.urls big_5.extraversion big_5.active big_5.assertive big_5.cheerful big_5.energetic big_5.friendly big_5.sociable big_5.openness big_5.adventurous big_5.artistic big_5.emotionally_aware big_5.imaginative big_5.intellectual big_5.liberal big_5.conscientiousness big_5.ambitious big_5.cautious big_5.disciplined big_5.dutiful big_5.organized big_5.self_assured big_5.neuroticism big_5.aggressive big_5.anxiety_prone big_5.impulsive big_5.melancholy big_5.self_conscious big_5.stress_prone big_5.agreeableness big_5.cooperative big_5.empathetic big_5.genuine big_5.generous big_5.humble big_5.trusting social_dynamics.social social_dynamics.affiliation social_dynamics.inward_focus social_dynamics.outward_focus social_dynamics.authentic social_dynamics.negations social_dynamics.clout drives.affiliation drives.achievement drives.risk_seeking drives.risk_aversion drives.risk_focus drives.power drives.reward cognition.analytical_thinking cognition.cognitive_processes cognition.causation cognition.certainty cognition.comparisons cognition.differentiation cognition.discrepancies cognition.insight cognition.tentative temporal_and_orientation.focus_past temporal_and_orientation.focus_present temporal_and_orientation.focus_future temporal_and_orientation.self_focus temporal_and_orientation.external_focus sallee.sentiment sallee.goodfeel sallee.badfeel sallee.emotionality sallee.non_emotion sallee.ambifeel sallee.admiration sallee.amusement sallee.excitement sallee.gratitude sallee.joy sallee.love sallee.anger sallee.boredom sallee.disgust sallee.fear sallee.sadness sallee.calmness sallee.curiosity sallee.surprise sparse_sallee.sentiment sparse_sallee.goodfeel sparse_sallee.badfeel sparse_sallee.emotionality sparse_sallee.non_emotion sparse_sallee.ambifeel sparse_sallee.admiration sparse_sallee.amusement sparse_sallee.excitement sparse_sallee.gratitude sparse_sallee.joy sparse_sallee.love sparse_sallee.anger sparse_sallee.boredom sparse_sallee.disgust sparse_sallee.fear sparse_sallee.sadness sparse_sallee.calmness sparse_sallee.curiosity sparse_sallee.surprise liwc15.analytical_thinking liwc15.clout liwc15.authentic liwc15.emotional_tone liwc15.six_plus_words liwc15.dictionary_words liwc15.function_words liwc15.pronouns liwc15.personal_pronouns liwc15.i liwc15.we liwc15.you liwc15.she_he liwc15.they liwc15.impersonal_pronouns liwc15.articles liwc15.prepositions liwc15.auxiliary_verbs liwc15.adverbs liwc15.conjunctions liwc15.negations liwc15.other_grammar liwc15.verbs liwc15.adjectives liwc15.comparisons liwc15.interrogatives liwc15.numbers liwc15.quantifiers liwc15.affective_processes liwc15.positive_emotion_words liwc15.negative_emotion_words liwc15.anxiety_words liwc15.anger_words liwc15.sad_words liwc15.social_processes liwc15.family liwc15.friends liwc15.female liwc15.male liwc15.cognitive_processes liwc15.insight liwc15.causation liwc15.discrepancies liwc15.tentative liwc15.certainty liwc15.differentiation liwc15.perceptual_processes liwc15.see liwc15.hear liwc15.feel liwc15.biological_processes liwc15.body liwc15.health liwc15.sexual liwc15.ingestion liwc15.drives liwc15.affiliation liwc15.achievement liwc15.power liwc15.reward liwc15.risk liwc15.time_orientation liwc15.focus_past liwc15.focus_present liwc15.focus_future liwc15.relativity liwc15.motion liwc15.space liwc15.time liwc15.personal_concerns liwc15.work liwc15.leisure liwc15.home liwc15.money liwc15.religion liwc15.death liwc15.informal_language liwc15.swear_words liwc15.netspeak liwc15.assent liwc15.nonfluencies liwc15.filler_words liwc15.all_punctuation liwc15.periods liwc15.commas liwc15.colons liwc15.semicolons liwc15.question_marks liwc15.exclamations liwc15.dashes liwc15.quotes liwc15.apostrophes liwc15.parentheses liwc15.other_punctuation disc_dimensions.bold_assertive_outgoing disc_dimensions.calm_methodical_reserved disc_dimensions.people_relationship_emotion_focus disc_dimensions.task_system_object_focus disc_dimensions.d_axis disc_dimensions.i_axis disc_dimensions.s_axis disc_dimensions.c_axis disc_dimensions.d_axis_proportional disc_dimensions.i_axis_proportional disc_dimensions.s_axis_proportional disc_dimensions.c_axis_proportional
890 20.698 43 0.182 0.026 0 0 0.000 0 66.450 62.411 55.284 48.455 81.421 66.757 63.074 41.371 40.570 54.250 65.634 54.417 25.722 57.026 69.416 62.244 57.394 49.055 82.194 45.669 55.019 47.886 63.021 44.525 54.177 36.178 41.548 56.744 73.118 47.333 52.590 78.366 73.302 61.003 66.740 70.850 49.212 55.632 65.193 63.932 36.991 63.737 49.212 54.764 50.819 38.352 43.132 39.839 53.508 43.867 35.257 51.084 42.596 53.626 30.739 39.515 44.103 34.763 0.045 0.113 0.025 0.045 0.084 0.059 0.138 0.079 0.236 0.764 0.019 0.063 0.000 0.012 0.031 0.005 0.044 0.058 0.035 0.032 0.019 0.011 0.000 0.000 0.000 -0.018 0.036 0.055 0.091 0.909 0.000 0.006 0.000 0.000 0.011 0.000 0.019 0.037 0.037 0.037 0.018 0.000 0.000 0.000 0 0.400 0.898 0.647 0.554 0.182 0.931 0.551 0.178 0.129 0.045 0.010 0.051 0.006 0.018 0.048 0.065 0.115 0.110 0.058 0.071 0.008 0.292 0.190 0.037 0.029 0.025 0.018 0.025 0.052 0.034 0.018 0.002 0.013 0.001 0.146 0.015 0.002 0.008 0.012 0.090 0.022 0.017 0.010 0.017 0.011 0.017 0.022 0.009 0.011 0.002 0.025 0.002 0.011 0.003 0.003 0.071 0.028 0.019 0.015 0.019 0.002 0.183 0.045 0.113 0.025 0.164 0.024 0.057 0.083 0.054 0.038 0.011 0.004 0.001 0.001 0.000 0.015 0.010 0.001 0.000 0.003 0.000 0.179 0.047 0.069 0.002 0.004 0.001 0.000 0.011 0.011 0.033 0.000 0.000 63.823 36.177 77.271 40.031 50.546 70.226 52.872 38.055 0.239 0.332 0.250 0.180
865 18.404 47 0.220 0.034 0 0 0.000 0 42.485 53.836 50.662 32.728 60.076 30.113 45.397 36.582 39.827 36.085 45.631 55.732 46.932 29.262 38.998 36.534 65.905 45.705 40.750 51.702 40.497 57.029 61.861 55.203 26.244 62.129 46.400 46.889 31.566 42.753 34.213 72.768 26.761 26.377 38.335 44.810 45.243 65.583 46.319 68.682 51.889 39.503 45.243 53.921 60.839 67.243 77.774 53.465 44.663 58.611 43.591 47.547 35.515 40.593 47.237 45.868 58.219 38.892 0.092 0.051 0.015 0.064 0.054 -0.048 0.060 0.108 0.190 0.810 0.021 0.014 0.016 0.017 0.007 0.017 0.004 0.053 0.004 0.009 0.041 0.041 0.007 0.005 0.000 -0.027 0.000 0.027 0.029 0.971 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0 0.655 0.528 0.721 0.471 0.220 0.911 0.533 0.163 0.118 0.064 0.014 0.018 0.018 0.003 0.045 0.087 0.125 0.084 0.027 0.074 0.017 0.262 0.183 0.030 0.017 0.015 0.025 0.018 0.046 0.029 0.017 0.005 0.001 0.005 0.087 0.001 0.001 0.010 0.010 0.109 0.035 0.015 0.014 0.021 0.008 0.031 0.009 0.002 0.003 0.002 0.016 0.001 0.013 0.000 0.002 0.083 0.022 0.018 0.027 0.013 0.013 0.158 0.092 0.051 0.015 0.148 0.023 0.065 0.064 0.099 0.054 0.031 0.002 0.005 0.001 0.006 0.002 0.000 0.000 0.000 0.002 0.000 0.164 0.053 0.061 0.002 0.001 0.001 0.000 0.008 0.012 0.023 0.000 0.002 46.899 53.101 46.507 48.643 47.763 46.703 49.695 50.823 0.245 0.240 0.255 0.261
861 19.568 44 0.175 0.030 0 0 0.000 0 70.568 59.646 59.470 62.395 64.962 62.851 77.836 58.559 60.519 63.493 70.082 77.188 31.287 52.408 69.509 67.047 45.136 53.267 67.189 71.737 52.856 58.775 63.372 44.478 49.280 63.687 36.883 46.705 65.898 41.511 64.661 61.507 67.566 60.181 55.095 66.385 43.052 45.901 61.124 44.396 48.364 64.092 43.052 41.079 56.821 44.294 47.226 47.002 52.790 48.191 37.116 49.933 56.529 50.881 36.729 53.826 35.579 31.719 0.044 0.111 0.022 0.027 0.078 0.069 0.137 0.068 0.249 0.751 0.045 0.028 0.010 0.012 0.015 0.030 0.053 0.022 0.002 0.012 0.012 0.021 0.013 0.011 0.000 0.009 0.029 0.020 0.056 0.944 0.007 0.009 0.007 0.008 0.002 0.009 0.000 0.008 0.000 0.000 0.000 0.000 0.000 0.007 0 0.475 0.903 0.343 0.735 0.175 0.929 0.548 0.177 0.105 0.027 0.006 0.053 0.007 0.012 0.072 0.067 0.130 0.095 0.058 0.071 0.015 0.287 0.182 0.043 0.027 0.024 0.028 0.019 0.046 0.036 0.010 0.001 0.002 0.003 0.136 0.003 0.013 0.000 0.010 0.094 0.015 0.016 0.019 0.014 0.017 0.022 0.033 0.014 0.007 0.009 0.016 0.000 0.014 0.002 0.001 0.064 0.019 0.009 0.021 0.019 0.003 0.178 0.044 0.111 0.022 0.148 0.023 0.067 0.063 0.056 0.023 0.012 0.009 0.006 0.005 0.002 0.001 0.001 0.000 0.000 0.000 0.000 0.175 0.050 0.059 0.002 0.000 0.001 0.000 0.005 0.014 0.039 0.000 0.005 54.296 45.704 72.998 53.560 53.927 62.956 57.761 49.476 0.241 0.281 0.258 0.221
1045 34.833 30 0.273 0.025 0 0 0.000 0 57.940 55.324 57.148 57.004 48.395 55.226 57.357 70.500 35.766 69.581 66.990 81.852 49.798 62.037 64.288 60.540 45.551 53.824 63.795 58.548 58.949 48.198 53.916 49.256 38.247 52.871 44.489 36.704 59.201 51.604 62.585 59.747 58.337 36.449 56.015 48.753 44.032 64.334 40.418 72.852 42.680 46.274 44.032 66.830 45.293 48.491 45.196 59.139 46.931 61.933 35.286 48.094 43.153 41.516 26.767 43.448 44.675 38.147 0.043 0.081 0.018 0.061 0.045 0.111 0.180 0.069 0.279 0.721 0.034 0.088 0.005 0.013 0.017 0.017 0.030 0.020 0.004 0.010 0.029 0.038 0.026 0.008 0.000 0.087 0.093 0.006 0.110 0.890 0.010 0.057 0.000 0.006 0.000 0.000 0.015 0.000 0.000 0.000 0.006 0.000 0.017 0.009 0 0.712 0.631 0.785 0.847 0.273 0.892 0.513 0.155 0.106 0.061 0.006 0.018 0.013 0.008 0.049 0.071 0.138 0.068 0.044 0.061 0.011 0.220 0.144 0.035 0.018 0.020 0.011 0.011 0.056 0.045 0.011 0.005 0.001 0.003 0.096 0.000 0.001 0.013 0.001 0.090 0.023 0.015 0.012 0.020 0.011 0.013 0.045 0.020 0.017 0.006 0.015 0.004 0.009 0.000 0.001 0.080 0.020 0.028 0.032 0.014 0.003 0.143 0.043 0.081 0.018 0.186 0.021 0.086 0.081 0.075 0.033 0.019 0.007 0.009 0.002 0.007 0.013 0.000 0.006 0.002 0.003 0.000 0.132 0.026 0.042 0.002 0.000 0.004 0.000 0.018 0.008 0.023 0.000 0.010 57.084 42.916 66.913 46.522 51.533 61.803 53.588 44.683 0.244 0.292 0.253 0.211
1050 27.632 38 0.230 0.044 0 0 0.003 0 58.443 81.444 47.045 48.083 67.941 50.379 56.533 57.115 44.233 65.836 53.485 73.225 44.172 49.734 49.915 40.276 31.710 38.771 63.297 51.260 62.454 48.062 64.197 37.292 42.034 48.722 40.572 34.185 61.033 41.134 77.726 68.609 59.762 37.709 44.127 44.375 43.970 79.430 21.669 80.706 35.107 33.720 43.970 49.375 39.988 44.578 41.999 48.138 46.837 71.329 25.376 35.032 36.594 38.284 28.882 35.302 40.144 29.100 0.051 0.071 0.010 0.090 0.015 0.024 0.126 0.102 0.277 0.723 0.051 0.024 0.028 0.016 0.024 0.015 0.021 0.041 0.001 0.000 0.045 0.006 0.010 0.010 0.004 -0.007 0.051 0.058 0.114 0.886 0.005 0.012 0.000 0.006 0.014 0.000 0.012 0.018 0.001 0.000 0.040 0.000 0.008 0.005 0 0.875 0.439 0.907 0.655 0.230 0.879 0.507 0.138 0.105 0.090 0.003 0.010 0.001 0.002 0.033 0.082 0.163 0.045 0.044 0.060 0.007 0.224 0.149 0.040 0.015 0.007 0.017 0.010 0.039 0.030 0.009 0.005 0.002 0.001 0.086 0.003 0.007 0.004 0.005 0.068 0.019 0.009 0.008 0.011 0.009 0.015 0.048 0.009 0.033 0.006 0.022 0.010 0.002 0.001 0.008 0.068 0.020 0.015 0.022 0.014 0.002 0.132 0.051 0.071 0.010 0.174 0.023 0.094 0.059 0.075 0.027 0.042 0.003 0.003 0.003 0.001 0.010 0.002 0.008 0.000 0.000 0.000 0.136 0.035 0.045 0.000 0.000 0.002 0.000 0.008 0.013 0.022 0.003 0.009 45.853 54.147 49.929 41.345 43.541 47.848 51.995 47.315 0.228 0.251 0.273 0.248
1021 24.902 41 0.235 0.027 0 0 0.000 0 38.703 43.958 64.152 26.795 32.610 43.596 41.691 72.597 29.597 73.788 57.228 85.597 57.226 60.138 44.763 34.217 49.674 46.660 60.262 57.370 42.859 63.902 67.147 56.554 39.826 65.449 51.794 48.751 50.492 41.254 57.662 62.726 51.219 42.008 40.777 43.725 46.877 56.214 47.260 71.646 33.865 51.769 46.877 60.909 38.890 62.061 48.660 52.187 37.900 57.089 43.177 52.588 41.555 37.684 40.633 35.668 55.490 43.752 0.031 0.104 0.013 0.046 0.056 -0.038 0.098 0.137 0.283 0.717 0.057 0.033 0.000 0.007 0.025 0.002 0.018 0.046 0.012 0.016 0.051 0.041 0.022 0.033 0.004 -0.011 0.031 0.041 0.099 0.901 0.027 0.004 0.000 0.000 0.017 0.000 0.000 0.006 0.000 0.000 0.033 0.000 0.009 0.027 0 0.629 0.715 0.767 0.591 0.235 0.900 0.541 0.164 0.102 0.046 0.016 0.037 0.000 0.003 0.059 0.074 0.134 0.079 0.050 0.073 0.006 0.248 0.167 0.044 0.015 0.017 0.013 0.008 0.067 0.042 0.024 0.008 0.006 0.005 0.084 0.001 0.002 0.000 0.003 0.108 0.032 0.018 0.008 0.025 0.011 0.025 0.034 0.018 0.009 0.007 0.024 0.012 0.009 0.000 0.002 0.076 0.024 0.024 0.025 0.008 0.004 0.146 0.031 0.104 0.013 0.158 0.018 0.081 0.064 0.057 0.034 0.015 0.003 0.003 0.002 0.001 0.008 0.003 0.002 0.000 0.000 0.002 0.139 0.039 0.032 0.002 0.000 0.001 0.000 0.027 0.004 0.027 0.001 0.005 56.218 43.782 55.761 37.229 45.748 55.989 49.410 40.373 0.239 0.292 0.258 0.211
505 15.781 32 0.261 0.042 0 0 0.000 0 61.967 63.142 54.070 54.383 73.084 51.182 54.295 59.034 54.188 41.538 37.271 61.566 60.056 77.502 57.730 54.357 23.464 40.302 67.156 60.241 56.646 33.561 54.250 29.531 51.323 31.100 30.830 30.250 59.657 48.057 42.005 50.813 65.483 39.277 73.145 39.784 41.275 72.866 24.546 75.749 43.305 34.080 41.275 47.454 17.948 50.010 35.696 68.265 32.532 61.989 41.941 33.771 61.891 41.120 34.126 45.849 54.752 37.840 0.050 0.077 0.012 0.077 0.020 0.101 0.134 0.033 0.194 0.806 0.029 0.064 0.000 0.026 0.009 0.005 0.050 0.010 0.000 0.005 0.011 0.012 0.007 0.010 0.002 0.043 0.052 0.009 0.062 0.938 0.000 0.030 0.000 0.028 0.000 0.000 0.011 0.009 0.000 0.000 0.000 0.009 0.000 0.000 0 0.713 0.445 0.830 0.769 0.261 0.893 0.550 0.160 0.097 0.077 0.000 0.020 0.000 0.000 0.063 0.091 0.149 0.077 0.057 0.063 0.012 0.257 0.158 0.038 0.018 0.028 0.014 0.026 0.040 0.034 0.006 0.002 0.000 0.002 0.075 0.008 0.002 0.002 0.006 0.105 0.032 0.008 0.014 0.020 0.020 0.020 0.012 0.004 0.006 0.002 0.002 0.000 0.000 0.000 0.000 0.069 0.016 0.014 0.040 0.004 0.000 0.139 0.050 0.077 0.012 0.152 0.020 0.071 0.063 0.077 0.055 0.014 0.004 0.010 0.000 0.000 0.006 0.000 0.000 0.002 0.004 0.000 0.184 0.057 0.075 0.006 0.000 0.004 0.002 0.006 0.004 0.026 0.002 0.002 55.334 44.666 53.862 52.681 53.991 54.593 49.049 48.508 0.262 0.265 0.238 0.235
512 11.907 43 0.193 0.030 0 0 0.000 0 45.410 39.472 58.285 41.857 44.310 46.742 43.511 56.757 41.269 65.974 69.961 50.533 44.820 45.925 55.585 69.404 53.919 70.143 42.592 56.086 29.349 68.986 59.109 66.598 69.189 62.769 55.869 69.903 39.169 32.934 66.935 57.483 32.704 21.636 50.547 57.325 34.806 59.791 61.319 68.343 52.262 53.011 34.806 77.160 54.014 34.193 42.159 40.997 59.516 34.837 64.702 78.985 87.677 40.853 49.523 35.629 63.283 45.713 0.035 0.148 0.023 0.053 0.078 0.022 0.119 0.097 0.233 0.767 0.019 0.077 0.000 0.007 0.003 0.015 0.013 0.018 0.002 0.008 0.033 0.029 0.013 0.007 0.002 0.001 0.021 0.020 0.041 0.959 0.000 0.021 0.000 0.000 0.000 0.000 0.000 0.012 0.003 0.000 0.000 0.000 0.000 0.000 0 0.244 0.734 0.715 0.844 0.193 0.928 0.570 0.223 0.131 0.053 0.000 0.057 0.006 0.016 0.092 0.045 0.125 0.111 0.064 0.061 0.018 0.316 0.191 0.057 0.018 0.041 0.010 0.023 0.057 0.045 0.012 0.002 0.002 0.004 0.115 0.004 0.000 0.000 0.006 0.156 0.039 0.031 0.008 0.027 0.031 0.033 0.025 0.006 0.006 0.014 0.010 0.008 0.002 0.000 0.000 0.074 0.006 0.035 0.016 0.023 0.002 0.207 0.035 0.148 0.023 0.133 0.014 0.084 0.041 0.061 0.053 0.002 0.000 0.012 0.000 0.000 0.012 0.006 0.000 0.002 0.002 0.000 0.199 0.076 0.051 0.012 0.000 0.008 0.000 0.002 0.004 0.045 0.002 0.000 62.819 37.181 58.431 45.725 53.595 60.585 46.610 41.232 0.265 0.300 0.231 0.204
492 9.840 50 0.209 0.030 0 0 0.000 0 61.947 51.836 59.433 54.708 59.546 60.607 61.527 47.855 59.184 46.199 51.642 57.517 48.536 30.641 57.657 60.192 48.844 53.481 50.420 63.754 48.274 56.681 53.092 60.355 52.540 56.458 39.843 61.852 55.239 48.255 49.647 51.503 53.572 59.843 61.272 71.862 40.229 33.811 79.981 40.962 66.168 68.734 40.229 62.142 77.513 54.325 82.774 37.096 58.020 38.737 44.975 34.176 58.491 48.331 39.397 56.769 53.370 38.387 0.030 0.163 0.022 0.004 0.108 0.048 0.137 0.089 0.255 0.745 0.046 0.047 0.002 0.013 0.025 0.026 0.030 0.021 0.003 0.013 0.023 0.018 0.004 0.016 0.000 0.031 0.050 0.019 0.076 0.924 0.013 0.023 0.000 0.013 0.004 0.017 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0 0.311 0.974 0.290 0.525 0.209 0.923 0.571 0.203 0.112 0.004 0.006 0.096 0.006 0.000 0.091 0.043 0.126 0.112 0.053 0.053 0.026 0.323 0.209 0.047 0.024 0.016 0.030 0.024 0.067 0.039 0.024 0.006 0.004 0.006 0.148 0.000 0.002 0.000 0.006 0.112 0.030 0.008 0.020 0.020 0.018 0.024 0.014 0.004 0.002 0.004 0.014 0.002 0.008 0.000 0.004 0.071 0.014 0.024 0.012 0.022 0.014 0.215 0.030 0.163 0.022 0.144 0.020 0.059 0.069 0.039 0.030 0.004 0.000 0.006 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.169 0.093 0.028 0.002 0.000 0.006 0.002 0.002 0.004 0.028 0.002 0.000 64.390 35.610 65.191 55.897 59.994 64.789 48.181 44.615 0.276 0.298 0.221 0.205

Segments: Analyze Scores

The SALLEE framework offers measures of emotions, so we might see which categories deviate the most in any of their segments:

# select the narrower SALLEE categories
emotions <- segmented_text[, grep("^sallee", colnames(segmented_text))[-(1:6)]]

# make contrasts for each segment
contrasts <- matrix(
  rep_len(c(1, 0, 0, 0, 1, 0, 0, 0, 1), nrow(segmented_text) * 3),
  ncol = 3, byrow = TRUE
)

# correlate emotion scores with contrasts
segment_deviations <- abs(cor(emotions, contrasts))

# select the 5 most deviating emotions
most_deviating <- segmented_text[, names(sort(-apply(
  segment_deviations, 1, max
))[1:5])]

Now we can look at those categories across segments:

splot(
  most_deviating ~ segment, segmented_text,
  laby = "Score", title = FALSE, mv.as.x = TRUE, type = "bar"
)

SALLEE scores by segment

The bar chart displays original values, which offers the clearest view of how meaningful the differences between segments might be, in addition to their statistical significance (which offers a rough guide to the reliability of differences, based on the variance within and between segments). By looking at the bar graph, you can immediately see that admiration shows some of the starkest differences between middle and early/late segments.

splot(
  scale(most_deviating) ~ segment, segmented_text,
  leg = "out", laby = "Score (Scaled)", title = FALSE, prat = c(4, 1)
)

scaled SALLEE scores by segment

The line charts, on the other hand, shows standardized values, effectively zooming in on the differences between segments. This more clearly shows, for example, that admiration and joy seem to be used as bookends in commencement speeches, peaking early and late, whereas more negative and intense emotions such as anger, disgust, and surprise peak in the middle section.