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JosuaKugler
2021-08-02 16:23:57 +02:00
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vignettes/hitlercomparison.Rmd
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@@ -69,41 +69,49 @@ For each party, we want to get a tibble of words with frequency.
```{r} ```{r}
#AfD #AfD
Worte <- str_extract_all(talks_by_fraktion$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[1]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) afdtotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> afd_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdtotal) -> afd_words
#AfD&Fraktionslos #AfD&Fraktionslos
Worte <- str_extract_all(talks_by_fraktion$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[2]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) afdundfraktionslostotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> afdundfraktionslos_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/afdundfraktionslostotal) -> afdundfraktionslos_words
#BÜNDNIS 90 / DIE GRÜNEN #BÜNDNIS 90 / DIE GRÜNEN
Worte <- str_extract_all(talks_by_fraktion$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[3]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) grünetotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> grüne_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/grünetotal) -> grüne_words
#CDU/CSU #CDU/CSU
Worte <- str_extract_all(talks_by_fraktion$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[4]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) cdutotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> cdu_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/cdutotal) -> cdu_words
#DIE LINKE #DIE LINKE
Worte <- str_extract_all(talks_by_fraktion$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[5]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) linketotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> linke_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/linketotal) -> linke_words
#FDP #FDP
Worte <- str_extract_all(talks_by_fraktion$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[6]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) fdptotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> fdp_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fdptotal) -> fdp_words
#Fraktionslos #Fraktionslos
Worte <- str_extract_all(talks_by_fraktion$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[7]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) fraktionslostotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> fraktionslos_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/fraktionslostotal) -> fraktionslos_words
#SPD #SPD
Worte <- str_extract_all(talks_by_fraktion$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[8]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) spdtotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> spd_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/spdtotal) -> spd_words
#NA #NA
Worte <- str_extract_all(talks_by_fraktion$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]] Worte <- str_extract_all(talks_by_fraktion$full_text[[9]], "\\b[a-zA-ZäöüÄÖÜß]+\\b")[[1]]
total = length(Worte) natotal = length(Worte)
tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/total) -> na_words tibble(Worte) %>% group_by(Worte) %>% count() %>% mutate(freq =n/natotal) -> na_words
#alle #alle
all_words <- bind_rows(afd_words, afdundfraktionslos_words, grüne_words, cdu_words, linke_words, fdp_words, fraktionslos_words, spd_words, na_words) all_words <- bind_rows(afd_words, afdundfraktionslos_words, grüne_words, cdu_words, linke_words, fdp_words, fraktionslos_words, spd_words, na_words)
total <- sum(all_words$n) total <- sum(all_words$n)
@@ -114,10 +122,64 @@ Now we want to extract the words that are more frequently used by a specific `fr
```{r} ```{r}
afd_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> afd_high_frequent afd_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> afd_high_frequent
afdundfraktionslos_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> afdundfraktionslos_high_frequent
grüne_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> grüne_high_frequent
cdu_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> cdu_high_frequent
linke_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> linke_high_frequent
fdp_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> fdp_high_frequent
fraktionslos_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> fraktionslos_high_frequent
spd_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> spd_high_frequent
na_words %>% transmute(freq, fraktion_n = n) %>% left_join(all_words) %>% transmute(fraktion_freq = freq, total_freq = part, fraktion_n, total_n = n, rel_quotient = fraktion_freq/total_freq, abs_quotient = fraktion_n/total_n) %>% arrange(-abs_quotient, -fraktion_n) %>% filter(rel_quotient > 1) -> na_high_frequent
``` ```
We compare these words with `hitlerwords`. We compare these words with `hitlerwords`.
```{r} ```{r}
afd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) afd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> afd_hitler_comparison
afdundfraktionslos_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> afdundfraktionslos_hitler_comparison
grüne_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> grüne_hitler_comparison
cdu_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> cdu_hitler_comparison
linke_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> linke_hitler_comparison
fdp_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> fdp_hitler_comparison
fraktionslos_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> fraktionslos_hitler_comparison
spd_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> spd_hitler_comparison
na_high_frequent %>% mutate(Worte = str_to_lower(Worte)) %>% inner_join(hitlerwords) -> na_hitler_comparison
#not unique
tibble(fraktion = c("AfD", "AfD und Fraktionslose", "Grüne", "CDU", "Linke", "FDP", "Fraktionslos", "SPD", "NA"),
absolute = c(nrow(afd_hitler_comparison), nrow(afdundfraktionslos_hitler_comparison), nrow(grüne_hitler_comparison), nrow(cdu_hitler_comparison), nrow(linke_hitler_comparison), nrow(fdp_hitler_comparison), nrow(fraktionslos_hitler_comparison), nrow(spd_hitler_comparison), nrow(na_hitler_comparison)),
total = c(nrow(afd_words), nrow(afdundfraktionslos_words), nrow(grüne_words), nrow(cdu_words), nrow(linke_words), nrow(fdp_words), nrow(fraktionslos_words), nrow(spd_words), nrow(na_words))
) %>% mutate(relative = absolute/total) -> hitler_compare
```
Dead code:
```r
1000*nrow(afd_hitler_comparison) / nrow(afd_words)
1000*nrow(afdundfraktionslos_hitler_comparison) / nrow(afdundfraktionslos_words)
1000*nrow(grüne_hitler_comparison) / nrow(grüne_words)
1000*nrow(cdu_hitler_comparison) / nrow(cdu_words)
1000*nrow(linke_hitler_comparison) / nrow(linke_words)
1000*nrow(fdp_hitler_comparison) / nrow(fdp_words)
1000*nrow(fraktionslos_hitler_comparison) / nrow(fraktionslos_words)
1000*nrow(spd_hitler_comparison) / nrow(spd_words)
1000*nrow(na_hitler_comparison) / nrow(na_words)
1000*sum(afd_hitler_comparison$fraktion_n) / afdtotal
1000*sum(afdundfraktionslos_hitler_comparison$fraktion_n) / afdundfraktionslostotal
1000*sum(grüne_hitler_comparison$fraktion_n) / grünetotal
1000*sum(cdu_hitler_comparison$fraktion_n) / cdutotal
1000*sum(linke_hitler_comparison$fraktion_n) / linketotal
1000*sum(fdp_hitler_comparison$fraktion_n) / fdptotal
1000*sum(fraktionslos_hitler_comparison$fraktion_n) / fraktionslostotal
1000*sum(spd_hitler_comparison$fraktion_n) / spdtotal
1000*sum(na_hitler_comparison$fraktion_n) / natotal
``` ```