aesopR ships with two sentiment-joined datasets so you can run sentiment workflows immediately (and reproducibly) without triggering downloads or interactive prompts:
Bing: categorical labels (positive, negative)
AFINN: numeric scores (e.g., -5 to +5)
These lexicons are bundled inside tidytext, which makes them lightweight for teaching and safe for non-interactive environments (like automated checks).
library(aesopR)A quick refresher: what’s in the sentiment datasets?
aesops_bing
aesops_bing
aesops_afinn
aesops_afinn
Bing: overall counts
table(aesops_bing$sentiment)AFINN: overall score distribution
summary(aesops_afinn$value)Teaching note: Ask students what it would mean for a fable to be “positive” or “negative.” Is sentiment the same thing as a moral tone? Often, not.
Bing: net sentiment per fable (positive minus negative)
bing_tab <- with(
aesops_bing,
table(fable_id, sentiment)
)
# Some fables may not have both labels present; guard with checks
pos <- if ("positive" %in% colnames(bing_tab)) bing_tab[, "positive"] else rep(0, nrow(bing_tab))
neg <- if ("negative" %in% colnames(bing_tab)) bing_tab[, "negative"] else rep(0, nrow(bing_tab))
bing_net <- pos - neg
bing_net <- sort(bing_net)
# Most negative
head(bing_net, 10)
# Most positive
tail(bing_net, 10)AFINN: mean score per fable
afinn_mean <- tapply(aesops_afinn$value, aesops_afinn$fable_id, mean)
afinn_mean <- sort(afinn_mean)
# Lowest mean scores
head(afinn_mean, 10)
# Highest mean scores
tail(afinn_mean, 10)Do Bing and AFINN “agree” on the extremes?
What might explain disagreement?
Are fables with “negative” language necessarily pessimistic?
Bing: which words are driving the sentiment?
fox_bing <- aesops_bing |> filter(fable_id == "005")
table(fox_bing$sentiment)
# Most frequent negative/positive words in this fable
sort(table(fox_bing$word[fox_bing$sentiment == "negative"]), decreasing = TRUE)[1:10]
sort(table(fox_bing$word[fox_bing$sentiment == "positive"]), decreasing = TRUE)[1:10]AFINN: strongest scored words
fox_afinn <- aesops_afinn |> filter(fable_id == "005")
summary(fox_afinn$value)
afinn_ordered <- fox_afinn[order(fox_afinn$value), c("word", "value")]
head(afinn_ordered, 8)
tail(afinn_ordered, 8)Invite students to read the moral and discuss whether “sentiment” captures:
the moral lesson
the emotional arc
the narrator’s perspective
irony/rationalization (“sour grapes”)
You can extend sentiment or emotion analysis using additional lexicons in the tidytext/textdata ecosystem.
Important practical note: Some lexicons (e.g., NRC) may require textdata to download them on demand. This can trigger interactive prompts in non-interactive environments (e.g., automated checks). For this reason, aesopR ships with Bing and AFINN only, and treats other lexicons as an optional extension.
This is an example pattern (not required for aesopR to work).
Some lexicons will be immediately available; others may require textdata.
if (requireNamespace("tidytext", quietly = TRUE)) {
# e.g., "loughran" is available via tidytext::get_sentiments()
# (designed for financial contexts; included here to illustrate extension)
lex <- tidytext::get_sentiments("loughran") |>
head(lex)
}If you plan to use lexicons that download through textdata, consider doing so in an interactive session and caching results in your own analysis project, rather than relying on on-demand downloads during automated runs.
These workflows are intentionally simple so they can serve as teaching tools.
Key methods questions to emphasize:
What does the lexicon measure?
What does it not measure (context, negation, sarcasm, narrative framing)?
How do pre-processing choices change results?
What would validation look like (human coding, triangulation, sensitivity checks)?