Textual Insights: Natural Language Processing (NLP) in R

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Many of the concepts and tasks in Natural Language Processing (NLP) can be accomplished using Python and R as well. Here’s a brief overview of how you can perform some common NLP tasks in R:

1. Tokenization:

Tokenization in R can be done using the tokenize_words function from the tokenizers package.

# Tokenization
library(tokenizers)

text <- "Natural Language Processing is fascinating!"
tokens <- unlist(tokenize_words(text))
print(tokens)

2. Stop Words Removal:

Stop words removal can be achieved using the tm package in R.

# Stop Words Removal
library(tm)

text <- "Natural Language Processing is fascinating!"
corpus <- Corpus(VectorSource(text))
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, stopwords("en"))
filtered_text <- unlist(sapply(corpus, as.character))
print(filtered_text)

3. Stemming and Lemmatization:

Stemming and lemmatization can be done using the tm and textstem packages.

# Stemming and Lemmatization
library(textstem)

text <- "Natural Language Processing is…