This project reproduces and extends the sentiment analysis example from Chapter 2 of Text Mining with R (Silge & Robinson, 2017). The base example uses Jane Austen’s novels to demonstrate how sentiment lexicons can trace emotional arcs across a narrative. The extension applies similar methods to a more charged question: how does sentiment and language differ across male online communities associated with loneliness and ideological grievance? Three subreddits were selected along a spectrum of radicalization: r/lonely, r/ForeverAlone, and r/mensrights. Posts were collected using the RedditExtractoR package and analyzed using three lexicons: the Bing lexicon for comparison with the base example, a custom manosphere lexicon built for this project, and sentimentr for context-aware sentence-level scoring. Notable communities from the original research design, including r/MGTOW and r/redpill, were inaccessible due to Reddit bans, a finding that is itself worth acknowledging.
library(tidytext)
library(janeaustenr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(stringr)
library(ggplot2)
library(tidyr)
library(textdata)
library(sentimentr)reddit_data <- readRDS("reddit_data.rds")tidy_books <- austen_books() |>
group_by(book) |>
mutate(
linenumber = row_number(),
chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
ignore_case = TRUE)))
) |>
ungroup()tidy_books <- tidy_books |>
unnest_tokens(word, text)nrc_joy <- get_sentiments("nrc") |>
filter(sentiment == "joy")
tidy_books |>
filter(book == "Emma") |>
inner_join(nrc_joy) |>
count(word, sort = TRUE)Joining with `by = join_by(word)`# A tibble: 301 × 2
word n
<chr> <int>
1 good 359
2 friend 166
3 hope 143
4 happy 125
5 love 117
6 deal 92
7 found 92
8 present 89
9 kind 82
10 happiness 76
# ℹ 291 more rowsjane_austen_sentiment <- tidy_books |>
inner_join(get_sentiments("bing"), relationship = "many-to-many") |>
count(book, index = linenumber %/% 80, sentiment) |>
pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) |>
mutate(sentiment = positive - negative)Joining with `by = join_by(word)`ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
Each bar represents net sentiment within an 80-line chunk of text. Austen’s novels trend positive overall, fitting her romantic style, but every novel dips negative at its dramatic turning points. Pride and Prejudice, for instance, turns sharply negative near its midpoint during Lydia’s elopement crisis. All six novels recover and end on a positive note, reflecting Austen’s signature happy endings.
Silge, J., & Robinson, D. (2017). Text Mining with R: A Tidy Approach. O’Reilly Media. https://www.tidytextmining.com
tidy_reddit <- reddit_data |>
unnest_tokens(word, text) |>
anti_join(stop_words)Joining with `by = join_by(word)`reddit_sentiment <- tidy_reddit |>
inner_join(get_sentiments("bing"), relationship = "many-to-many") |>
count(subreddit, sentiment) |>
pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) |>
mutate(
net_sentiment = positive - negative,
ratio = positive / (positive + negative)
)Joining with `by = join_by(word)`tidy_reddit |>
inner_join(get_sentiments("bing"), relationship = "many-to-many") |>
filter(sentiment == "negative") |>
count(subreddit, word, sort = TRUE) |>
group_by(subreddit) |>
slice_max(n, n = 10) |>
ggplot(aes(x = reorder(word, n), y = n, fill = subreddit)) +
geom_col(show.legend = FALSE) +
facet_wrap(~subreddit, scales = "free_y") +
coord_flip() +
labs(
title = "Top Negative Words by Subreddit",
x = "Word",
y = "Count"
) +
theme_minimal()Joining with `by = join_by(word)`
ggplot(reddit_sentiment, aes(x = subreddit, y = net_sentiment, fill = subreddit)) +
geom_col(show.legend = FALSE) +
labs(
title = "Net Sentiment Across Subreddits",
x = "Subreddit",
y = "Net Sentiment (Positive - Negative)"
) +
theme_minimal()
manosphere_lexicon <- tibble(
word = c(
# Dehumanizing slurs
"femoid", "roastie", "whore", "slut", "thot", "cunt", "bitch",
"gold digger", "used goods",
# Incel/manosphere coded language
"hypergamy", "awalt", "blackpill", "chad", "stacy", "betabux",
"smv", "gynocentric", "misandry", "redpill", "alpha", "beta",
"sexual marketplace", "cock carousel", "pair bonding", "wall",
"female nature", "beta male",
# Grievance framing
"divorce rape", "false accusations", "female privilege",
"family court", "child support trap", "men are oppressed",
"gynocentric society", "misandry",
# Dehumanizing framing
"females", "validation", "attention seekers", "easy mode",
"women always", "women only", "modern women", "female privilege",
"used goods", "logic vs emotion", "men build"
),
score = c(
# Slurs
-4, -4, -3, -3, -3, -4, -3,
-3, -3,
# Coded language
-2, -3, -3, -1, -1, -2,
-2, -2, -2, -2, -1, -1,
-2, -4, -2, -2,
-2, -2,
# Grievance
-3, -2, -2,
-2, -3, -2,
-2, -2,
# Framing
-1, -1, -2, -2,
-2, -2, -2, -2,
-3, -2, -2
),
lexicon = "manosphere"
)manosphere_scores <- tidy_reddit |>
inner_join(manosphere_lexicon, by = "word") |>
group_by(subreddit) |>
summarise(
manosphere_score = sum(score),
term_count = n()
)Warning in inner_join(tidy_reddit, manosphere_lexicon, by = "word"): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 34332 of `x` matches multiple rows in `y`.
ℹ Row 7 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.manosphere_scores <- tidy_reddit |>
inner_join(manosphere_lexicon, by = "word", relationship = "many-to-many") |>
group_by(subreddit) |>
summarise(
manosphere_score = sum(score),
term_count = n()
)ggplot(manosphere_scores, aes(x = subreddit, y = manosphere_score, fill = subreddit)) +
geom_col(show.legend = FALSE) +
labs(
title = "Manosphere Language Score by Subreddit",
x = "Subreddit",
y = "Manosphere Score (lower = more extreme)"
) +
theme_minimal()
tidy_reddit |>
inner_join(manosphere_lexicon, by = "word", relationship = "many-to-many") |>
count(subreddit, word, sort = TRUE) |>
group_by(subreddit) |>
slice_max(n, n = 5)# A tibble: 16 × 3
# Groups: subreddit [3]
subreddit word n
<chr> <chr> <int>
1 r/ForeverAlone bitch 4
2 r/ForeverAlone validation 2
3 r/ForeverAlone alpha 1
4 r/ForeverAlone beta 1
5 r/ForeverAlone wall 1
6 r/lonely bitch 2
7 r/lonely wall 1
8 r/mensrights misandry 64
9 r/mensrights females 7
10 r/mensrights validation 3
11 r/mensrights alpha 1
12 r/mensrights beta 1
13 r/mensrights cunt 1
14 r/mensrights gynocentric 1
15 r/mensrights hypergamy 1
16 r/mensrights wall 1reddit_sentimentr <- sentiment_by(
get_sentences(reddit_data$text[!is.na(reddit_data$text)]),
by = list(reddit_data$subreddit[!is.na(reddit_data$text)])
)
reddit_sentimentr <- reddit_sentimentr |>
rename(subreddit = `reddit_data$subreddit[!is.na(reddit_data$text)]`)ggplot(reddit_sentimentr, aes(x = subreddit, y = ave_sentiment, fill = subreddit)) +
geom_col(show.legend = FALSE) +
labs(
title = "Average Sentiment by Subreddit (sentimentr)",
x = "Subreddit",
y = "Average Sentiment Score"
) +
theme_minimal()
Austen’s novels offer clean, formal text written by a single author, where sentiment shifts map neatly onto narrative structure. Reddit is the opposite: thousands of voices, informal language, sarcasm, and community slang that standard lexicons were never built to handle. The analysis is messier, but the questions are more urgent.
Bing found all three subreddits net negative but struggled to distinguish them meaningfully. The custom manosphere lexicon told a sharper story. r/mensrights produced 80 ideological term hits versus 9 in r/ForeverAlone and 3 in r/lonely, suggesting organized grievance rather than personal pain. Sentimentr, which handles negation and context, produced the cleanest gradient with r/mensrights most negative and r/ForeverAlone closest to neutral.
The most radicalized communities in the original design, r/MGTOW and r/redpill, were banned before data collection, so this analysis captures a moderate slice of the manosphere. Many manosphere terms are context-dependent, and scoring them uniformly negative introduces noise. Standard lexicons may also misclassify sarcasm and slang. Finally, only post text was collected since comments were excluded due to API limitations.