Sentiment Analysis on LOTR

#install.packages('tidytext')
#install.packages('textdata')
#install.packages('wordcloud')
#install.packages('tm')
library(tidytext)

## Warning: package 'tidytext' was built under R version 4.3.3

library(textdata)

## Warning: package 'textdata' was built under R version 4.3.3

library(wordcloud)

## Warning: package 'wordcloud' was built under R version 4.3.3

## Loading required package: RColorBrewer

library(tidyverse)

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## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2

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library(dplyr)
library(stringr)
library(tm)

## Warning: package 'tm' was built under R version 4.3.3

## Loading required package: NLP
## 
## Attaching package: 'NLP'
## 
## The following object is masked from 'package:ggplot2':
## 
##     annotate

#install.packages('devtools')

#install.packages('reticulate')
library(reticulate)

## Warning: package 'reticulate' was built under R version 4.3.3

#install_miniconda()

devtools::install_github("farach/huggingfaceR")

## Skipping install of 'huggingfaceR' from a github remote, the SHA1 (a9dc96af) has not changed since last install.
##   Use `force = TRUE` to force installation

LOTR Script, with the movie, the character, and the line, thanks to github.com/tianyigu

LOTR <-read_csv("https://raw.githubusercontent.com/GuillermoCharlesSchneider/DATA-607/main/HW10/lotrscript.csv")

## Rows: 2390 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): char, dialog, movie
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

get_sentiments("afinn")

## # A tibble: 2,477 × 2
##    word       value
##    <chr>      <dbl>
##  1 abandon       -2
##  2 abandoned     -2
##  3 abandons      -2
##  4 abducted      -2
##  5 abduction     -2
##  6 abductions    -2
##  7 abhor         -3
##  8 abhorred      -3
##  9 abhorrent     -3
## 10 abhors        -3
## # ℹ 2,467 more rows

Movies split into individual words

LOTR$dialog <- removePunctuation(LOTR$dialog) #Remove all punctuation marks
LOTR$dialog <- stripWhitespace(LOTR$dialog) #Remove excess whitespace
LOTR$dialog <- tolower(LOTR$dialog) #Make all characters lowercase
LOTR$dialog <- removeNumbers(LOTR$dialog)

tidy_LOTR <- LOTR %>%
  group_by(movie) %>%
  ungroup() %>%
  unnest_tokens(word, dialog)

Joyful Words in The Return of the King

nrc_joy <- get_sentiments("nrc") %>% 
  filter(sentiment == "joy")

tidy_LOTR %>%
  filter(movie == "The Return of the King Movie Script") %>%
  inner_join(nrc_joy) %>%
  count(word, sort = TRUE)

## Joining with `by = join_by(word)`

## # A tibble: 70 × 2
##    word         n
##    <chr>    <int>
##  1 merry       15
##  2 precious    15
##  3 hope        11
##  4 love        11
##  5 peace        9
##  6 tree         9
##  7 save         8
##  8 victory      8
##  9 good         7
## 10 journey      6
## # ℹ 60 more rows

Sentiment change through the books

tidy_LOTR %>% 
  count(char) %>%
  arrange(desc(n)) %>%
  slice(1:15) %>%
  ggplot(aes(x=reorder(char, n), y=n)) +
  geom_bar(stat="identity", aes(fill=n), show.legend=FALSE) + 
  geom_label(aes(label=n)) +
  scale_fill_gradient(low="red", high="blue") +
  labs(x="Character", y="Lines of dialogue",
       title="Lines of dialogue per character (absolute values)") +  
  coord_flip() +
  theme_bw()

Just The Two Towers df by word

twoTowers <- tidy_LOTR %>% 
  filter(movie == "The Two Towers Movie Script")

twoTowers

## # A tibble: 10,174 × 3
##    char     movie                       word       
##    <chr>    <chr>                       <chr>      
##  1 GANDALF: The Two Towers Movie Script saurons    
##  2 GANDALF: The Two Towers Movie Script wrath      
##  3 GANDALF: The Two Towers Movie Script will       
##  4 GANDALF: The Two Towers Movie Script be         
##  5 GANDALF: The Two Towers Movie Script terrible   
##  6 GANDALF: The Two Towers Movie Script his        
##  7 GANDALF: The Two Towers Movie Script retribution
##  8 GANDALF: The Two Towers Movie Script swift      
##  9 GANDALF: The Two Towers Movie Script the        
## 10 GANDALF: The Two Towers Movie Script battle     
## # ℹ 10,164 more rows

Comparing the sentiment packages

afinn <- twoTowers %>% 
  inner_join(get_sentiments("afinn")) %>% 
  group_by(char) %>% 
  summarise(sentiment = sum(value)) %>% 
  mutate(method = "AFINN")

## Joining with `by = join_by(word)`

bing_and_nrc <- bind_rows(
  twoTowers %>% 
    inner_join(get_sentiments("bing")) %>%
    mutate(method = "Bing et al."),
  twoTowers %>% 
    inner_join(get_sentiments("nrc") %>% 
                 filter(sentiment %in% c("positive", 
                                         "negative"))
    ) %>%
    mutate(method = "NRC"))

## Joining with `by = join_by(word)`
## Joining with `by = join_by(word)`

## Warning in inner_join(., get_sentiments("nrc") %>% filter(sentiment %in% : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 502 of `x` matches multiple rows in `y`.
## ℹ Row 464 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.

Bing Sentiment, individual words, all books

LOTR_word_counts <- tidy_LOTR %>%
  inner_join(get_sentiments("bing")) %>%
  count(word, sentiment, sort = TRUE) %>%
  ungroup()

## Joining with `by = join_by(word)`

LOTR_word_counts

## # A tibble: 501 × 3
##    word     sentiment     n
##    <chr>    <chr>     <int>
##  1 master   positive     58
##  2 dead     negative     48
##  3 good     positive     46
##  4 merry    positive     41
##  5 death    negative     35
##  6 precious positive     35
##  7 well     positive     33
##  8 like     positive     31
##  9 great    positive     27
## 10 right    positive     27
## # ℹ 491 more rows

Contribution to sentiment

LOTR_word_counts %>%
  group_by(sentiment) %>%
  slice_max(n, n = 10) %>% 
  ungroup() %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(n, word, fill = sentiment)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~sentiment, scales = "free_y") +
  labs(x = "Contribution to sentiment",
       y = NULL)

Love a good word cloud

tidy_LOTR %>%
  anti_join(stop_words) %>%
  count(word) %>%
  with(wordcloud(word, n, max.words = 100))

## Joining with `by = join_by(word)`

trying hugging face

https://heartbeat.comet.ml/using-hugging-face-transformers-for-sentiment-analysis-in-r-6b100c025a2e

i got too lost in the sauce. that was fun tho! i dont fully understand exactly what this package is doing. it seems cool, using the already trained nlp model, check the model results against the predicted scores. its also running python in R?

#hf_python_depends('transformers')
library(huggingfaceR)

distilBERT <- hf_load_pipeline(
  model_id = "distilbert-base-uncased-finetuned-sst-2-english", 
  task = "text-classification"
  )

## 
## 
## distilbert-base-uncased-finetuned-sst-2-english is ready for text-classification

reticulate::py_install("transformers")

## + "C:/Users/guill/AppData/Local/r-miniconda/condabin/conda.bat" "install" "--yes" "--prefix" "C:/Users/guill/AppData/Local/r-miniconda/envs/huggingfaceR" "-c" "conda-forge" "transformers"

transformers <- import("transformers")
model_name <- "nlptown/bert-base-multilingual-uncased-sentiment"
tokenizer <- transformers$BertTokenizer$from_pretrained(model_name)
model <- transformers$BertForSequenceClassification$from_pretrained(model_name)

inputs <- lapply(LOTR$dialog, tokenizer$encode_plus, add_special_tokens = TRUE, 
                 return_tensors = "pt")

torch <- import("torch")
outputs <- lapply(inputs, function(input) {
  with(torch$no_grad(), {
    model(input_ids = input$input_ids, token_type_ids = input$token_type_ids, 
          attention_mask = input$attention_mask)$logits
  })
})

predictions <- sapply(outputs, function(output) {
  torch$argmax(output)$item() 
})

threshold <- 2.5
LOTR$binary_scores <- ifelse(predictions >= threshold, 1, 0)

LOTR %>% 
  mutate(sentiment = ifelse(LOTR$binary_scores > 0, "Positive", "Negative")) %>%
  group_by(sentiment) %>%
  summarise(count = n())

## # A tibble: 2 × 2
##   sentiment count
##   <chr>     <int>
## 1 Negative   1235
## 2 Positive   1155

LOTR$sentimentresult <- ifelse(LOTR$binary_scores >0 , "Positive", "Negative")
LOTR$sentimentresult

##    [1] "Negative" "Negative" "Negative" "Negative" "Negative" "Negative"
##    [7] "Positive" "Negative" "Positive" "Positive" "Negative" "Negative"
##   [13] "Negative" "Positive" "Positive" "Positive" "Positive" "Negative"
##   [19] "Negative" "Negative" "Positive" "Positive" "Positive" "Positive"
##   [25] "Negative" "Negative" "Positive" "Negative" "Positive" "Positive"
##   [31] "Positive" "Negative" "Positive" "Positive" "Positive" "Positive"
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##   [43] "Positive" "Positive" "Negative" "Positive" "Positive" "Negative"
##   [49] "Positive" "Negative" "Negative" "Positive" "Negative" "Negative"
##   [55] "Positive" "Positive" "Negative" "Positive" "Positive" "Negative"
##   [61] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
##   [67] "Positive" "Positive" "Positive" "Negative" "Positive" "Positive"
##   [73] "Positive" "Positive" "Negative" "Positive" "Positive" "Positive"
##   [79] "Negative" "Negative" "Negative" "Positive" "Negative" "Negative"
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##   [91] "Positive" "Negative" "Negative" "Negative" "Negative" "Negative"
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##  [103] "Negative" "Negative" "Positive" "Positive" "Negative" "Negative"
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## [1381] "Negative" "Negative" "Positive" "Negative" "Positive" "Positive"
## [1387] "Positive" "Positive" "Positive" "Negative" "Positive" "Negative"
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## [1471] "Positive" "Positive" "Positive" "Positive" "Positive" "Negative"
## [1477] "Positive" "Positive" "Positive" "Negative" "Negative" "Negative"
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## [1513] "Positive" "Negative" "Negative" "Negative" "Positive" "Positive"
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## [1591] "Negative" "Negative" "Negative" "Positive" "Positive" "Positive"
## [1597] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
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## [1657] "Negative" "Negative" "Negative" "Negative" "Negative" "Negative"
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## [1783] "Positive" "Negative" "Positive" "Positive" "Positive" "Positive"
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## [1831] "Negative" "Negative" "Positive" "Positive" "Positive" "Negative"
## [1837] "Negative" "Negative" "Negative" "Negative" "Negative" "Positive"
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## [1849] "Positive" "Positive" "Positive" "Negative" "Positive" "Positive"
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## [1867] "Positive" "Positive" "Positive" "Negative" "Positive" "Positive"
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## [1879] "Positive" "Positive" "Positive" "Negative" "Negative" "Negative"
## [1885] "Negative" "Negative" "Positive" "Negative" "Positive" "Negative"
## [1891] "Positive" "Negative" "Negative" "Negative" "Positive" "Negative"
## [1897] "Negative" "Negative" "Positive" "Positive" "Positive" "Positive"
## [1903] "Positive" "Positive" "Positive" "Negative" "Positive" "Negative"
## [1909] "Negative" "Negative" "Positive" "Negative" "Positive" "Negative"
## [1915] "Negative" "Negative" "Negative" "Positive" "Negative" "Negative"
## [1921] "Positive" "Negative" "Positive" "Positive" "Negative" "Positive"
## [1927] "Negative" "Positive" "Negative" "Negative" "Negative" "Negative"
## [1933] "Positive" "Positive" "Negative" "Negative" "Positive" "Negative"
## [1939] "Negative" "Negative" "Negative" "Positive" "Positive" "Negative"
## [1945] "Negative" "Positive" "Positive" "Negative" "Negative" "Negative"
## [1951] "Negative" "Positive" "Negative" "Negative" "Negative" "Negative"
## [1957] "Positive" "Negative" "Negative" "Negative" "Negative" "Positive"
## [1963] "Positive" "Positive" "Positive" "Positive" "Negative" "Negative"
## [1969] "Negative" "Negative" "Positive" "Positive" "Negative" "Negative"
## [1975] "Positive" "Negative" "Positive" "Positive" "Positive" "Positive"
## [1981] "Negative" "Negative" "Positive" "Positive" "Positive" "Positive"
## [1987] "Negative" "Negative" "Negative" "Negative" "Negative" "Positive"
## [1993] "Negative" "Positive" "Positive" "Positive" "Negative" "Negative"
## [1999] "Positive" "Negative" "Negative" "Negative" "Negative" "Negative"
## [2005] "Negative" "Negative" "Positive" "Negative" "Positive" "Positive"
## [2011] "Positive" "Positive" "Negative" "Positive" "Positive" "Positive"
## [2017] "Negative" "Positive" "Positive" "Positive" "Negative" "Positive"
## [2023] "Positive" "Negative" "Positive" "Positive" "Positive" "Positive"
## [2029] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
## [2035] "Positive" "Negative" "Negative" "Negative" "Negative" "Positive"
## [2041] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
## [2047] "Positive" "Negative" "Positive" "Positive" "Negative" "Positive"
## [2053] "Positive" "Negative" "Positive" "Negative" "Positive" "Positive"
## [2059] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
## [2065] "Positive" "Negative" "Positive" "Negative" "Negative" "Positive"
## [2071] "Negative" "Positive" "Positive" "Negative" "Negative" "Positive"
## [2077] "Positive" "Positive" "Positive" "Positive" "Negative" "Positive"
## [2083] "Positive" "Negative" "Positive" "Positive" "Positive" "Negative"
## [2089] "Positive" "Negative" "Negative" "Negative" "Positive" "Positive"
## [2095] "Negative" "Negative" "Negative" "Negative" "Positive" "Positive"
## [2101] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
## [2107] "Positive" "Positive" "Positive" "Positive" "Negative" "Positive"
## [2113] "Positive" "Negative" "Positive" "Positive" "Negative" "Positive"
## [2119] "Positive" "Positive" "Positive" "Positive" "Positive" "Negative"
## [2125] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
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## [2143] "Positive" "Positive" "Negative" "Negative" "Positive" "Negative"
## [2149] "Positive" "Positive" "Positive" "Positive" "Negative" "Positive"
## [2155] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
## [2161] "Negative" "Negative" "Positive" "Positive" "Positive" "Positive"
## [2167] "Negative" "Positive" "Positive" "Negative" "Positive" "Negative"
## [2173] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
## [2179] "Positive" "Positive" "Negative" "Positive" "Negative" "Negative"
## [2185] "Positive" "Positive" "Positive" "Negative" "Negative" "Positive"
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## [2197] "Negative" "Negative" "Negative" "Negative" "Negative" "Positive"
## [2203] "Negative" "Negative" "Negative" "Negative" "Negative" "Negative"
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## [2215] "Positive" "Negative" "Negative" "Positive" "Negative" "Negative"
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## [2227] "Negative" "Negative" "Positive" "Negative" "Positive" "Positive"
## [2233] "Negative" "Positive" "Negative" "Negative" "Positive" "Negative"
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## [2287] "Positive" "Positive" "Negative" "Negative" "Positive" "Negative"
## [2293] "Negative" "Negative" "Negative" "Positive" "Negative" "Negative"
## [2299] "Positive" "Negative" "Positive" "Positive" "Negative" "Positive"
## [2305] "Negative" "Negative" "Negative" "Positive" "Positive" "Positive"
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## [2323] "Negative" "Negative" "Positive" "Positive" "Positive" "Negative"
## [2329] "Negative" "Negative" "Positive" "Positive" "Negative" "Positive"
## [2335] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
## [2341] "Negative" "Positive" "Positive" "Negative" "Positive" "Negative"
## [2347] "Positive" "Positive" "Negative" "Positive" "Negative" "Positive"
## [2353] "Negative" "Negative" "Negative" "Positive" "Negative" "Negative"
## [2359] "Positive" "Negative" "Negative" "Negative" "Negative" "Negative"
## [2365] "Positive" "Positive" "Negative" "Positive" "Negative" "Negative"
## [2371] "Positive" "Negative" "Negative" "Positive" "Positive" "Positive"
## [2377] "Negative" "Positive" "Negative" "Negative" "Positive" "Negative"
## [2383] "Negative" "Positive" "Positive" "Positive" "Positive" "Positive"
## [2389] "Negative" "Negative"

I think i must’ve implemented this confusion matrix wrong?

confusion_matrix <- table(unlist(predictions), unlist(LOTR$binary_scores))
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
precision <- diag(confusion_matrix) / colSums(confusion_matrix)
recall <- diag(confusion_matrix) / rowSums(confusion_matrix)

## Warning in diag(confusion_matrix)/rowSums(confusion_matrix): longer object
## length is not a multiple of shorter object length

confusion_matrix

##    
##       0   1
##   0 764   0
##   1 110   0
##   2 361   0
##   3   0 219
##   4   0 936

accuracy

## [1] 0.3196653

precision

##         0         1 
## 0.6186235 0.0000000

recall

##         0         1         2         3         4 
## 1.0000000 0.0000000 2.1163435 0.0000000 0.8162393

HW 10

Guillermo Schneider

2024-03-31

Sentiment Analysis on LOTR

trying hugging face