TF-IDF assigns a weight to each term based on its occurrence in a document and the total number of words in the document, normalizing the importance of terms within the document.

TF-IDF measures the similarity between documents by comparing the term frequency of words in each document to the average frequency across the corpus, without considering document length or term distribution.

TF-IDF calculates the frequency of each term in a document and multiplies it by the number of documents containing that term, to evaluate how relevant a term is to the document.

TF-IDF measures a word's importance within a document relative to a collection or corpus, adjusting for the fact that some words occur more frequently across all documents.

The get_sentiments function is used to load and return sentiment lexicons, such as AFINN or Bing, from the textdata package.

The get_sentiments function performs sentiment analysis by analyzing the frequency of keywords and their associated sentiment values in a given text to estimate the emotional tone.

The get_sentiments function evaluates the emotional tone of a text by calculating sentiment scores using a machine learning model that has been trained on a large corpus of annotated text data.

The get_sentiments function generates sentiment scores based on the context of the text by analyzing various linguistic features and contextual cues to determine the overall sentiment expressed.

Words that are used to stop a sentence

Words that must always be included in text analysis

The most common words in a language, though there isn't a universal list used by all NLP tools

Rare and complex words in a language

It breaks the text into ngrams, which are sequences of 1 character

It combines multiple words into one token

It breaks the text into ngrams, which are sequences of 1 word or token

It breaks the text into individual characters

Breaks the text into units as described by the token argument

Sorts the tokens alphabetically

Combines multiple text units into a single token

Deletes tokens that are not in the dictionary

ggplot(data = mpg ) + geom_boxplot(aes(x = class, y=hwy, fill=class)) + labs(y = "Highway miles per gallon")

That will set a local path which will not work in any machine other than yours

That will set a global path which will not work in any machine other than yours

It is a good idea since it will set a global path which will work in any machine

It is a good idea since it will set a local path which will work in any machine

remove whitespace from the start and end of a string

remove whitespace from the end of a string only

remove whitespace from the start of a string only

Unreliable formatting at the source

Data broken into many pages

Data arriving in multiple excel file formats

It can't be automated