pacman::p_load(tidyverse, readtext,
quanteda, tidytext)In-class Exercise 5a
5.1 An exploration of VAST Challenge 2024 - MC1 data
5.1.1 Loading the necessary R packages
5.1.2 Loading the data
data_folder <- "data/MC1/articles"5.1.3 Text sensing to extract text
text_data <- readtext(paste0("data/MC1/articles",
"/*"))OR the below code chunk can be utilised as well
text_data <- readtext("data/MC1/articles")5.2 Basic tokenisation
usenet_words <- text_data %>%
unnest_tokens(word, text) %>% #reading the text data
filter(str_detect(word, "[a-z']$"),
!word %in% stop_words$word) #remove stop wordsusenet_words %>%
count(word, sort = TRUE)readtext object consisting of 3260 documents and 0 docvars.
# A data frame: 3,260 × 3
word n text
<chr> <int> <chr>
1 fishing 2177 "\"\"..."
2 sustainable 1525 "\"\"..."
3 company 1036 "\"\"..."
4 practices 838 "\"\"..."
5 industry 715 "\"\"..."
6 transactions 696 "\"\"..."
# ℹ 3,254 more rows
Observations- Most common words are: fishing, sustainable and company
5.2.1 Creating a table to observe word counts
temp_table <- usenet_words %>%
count(word, sort = TRUE)5.2.2 Using corpus to read text data
corpus_text <- corpus(text_data)
summary(corpus_text, 5)Corpus consisting of 338 documents, showing 5 documents:
Text Types Tokens Sentences
Alvarez PLC__0__0__Haacklee Herald.txt 206 433 18
Alvarez PLC__0__0__Lomark Daily.txt 102 170 12
Alvarez PLC__0__0__The News Buoy.txt 90 200 9
Alvarez PLC__0__1__Haacklee Herald.txt 96 187 8
Alvarez PLC__0__1__Lomark Daily.txt 241 504 21
To separate the data; into 2 columns X & Y.
text_data_splitted <- text_data %>%
separate_wider_delim("doc_id",
delim = "__0__",
names = c("X", "Y"),
too_few = "align_end")Some text are starting with “1” hence the split does not occur