| Title: | An Easy Text and Sentiment Analysis Library |
| Version: | 0.1.9 |
| Maintainer: | Simi Kafaru <kafarusimileoluwa@gmail.com> |
| Description: | Implement text and sentiment analysis with 'texter'. Generate sentiment scores on text data and also visualize sentiments. 'texter' allows you to quickly generate insights on your data. It includes support for lexicons such as 'NRC' and 'Bing'. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/simmieyungie/texter |
| BugReports: | https://github.com/simmieyungie/texter/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Imports: | dplyr, plyr, ggplot2, magrittr, stringr, purrr, stopwords, textdata, tidytext, tidyr |
| Depends: | R (≥ 2.14) |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2021-09-20 13:58:13 UTC; SIMIYOUNG |
| Author: | Simi Kafaru [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2021-09-20 14:20:02 UTC |
This is the first data to be included in my package
Description
it contains news articles on brexits
Author(s)
SimiKafaru kafarusimileoluwa@gmail.com
Get the number of times a vector of words occurs
Description
This function retrieves the number of times each word in a corpus occurs. It returns a dataframe containing the word and the corresponding counts
Usage
counter(word_vec, words)
Arguments
word_vec |
This is the corpus you want to the word frequency extracted from |
words |
This is a vector of words you want to retrieve their frequency counts |
Value
a data frame object. A data frame object of strings and their corresponding count
This is the first data to be included in my package
Description
it contains tweets on doge coin collected using twitter API
Author(s)
SimiKafaru kafarusimileoluwa@gmail.com
This data was saved NRC word-emotion association lexicon
Description
The dataset is saved from the textdatahttps://github.com/EmilHvitfeldt/textdata/blob/master/R/lexicon_nrc.R for easier access
Value
A tibble with 13,901 rows and 4 variables:
- word
An English word
- sentiment
Indicator for sentiment or emotion: "negative", "positive", "anger", "anticipation", "disgust", "fear", "joy", "sadness", "surprise", or "trust"
Source
http://saifmohammad.com/WebPages/lexicons.html
Easily remove Punctuation from Text
Description
This function will help you remove punctuation and numbers from your text easily
Usage
removeNumPunct(x)
Arguments
x |
is the text column you want the punctuation and texts removed from |
Value
a character vector.
Examples
{
removeNumPunct("is this your number? 01234")
}
A function to help you remove URLs from text
Description
This function helps remove URLs from text, particularly designed for tweets
Usage
removeURL(x)
Arguments
x |
is the text value you want to extract the texts from |
Value
a character vector.
Get the overall weight of emotions conveyed in a corpus
Description
This function will help you extract the weight of emotions conveyed in a tweet
Usage
sentimentAnalyzer(word_vec, details)
Arguments
word_vec |
This is the corpus you want to extract the sentiments from |
details |
(A TRUE/FALSE value): If TRUE you get a more robust distribution of these emotions. FALSE is summarised as Positive or Negative |
Value
a data frame object. A data frame of each emotions and their corresponding weight in text
Examples
sentimentAnalyzer(doge$text, details = TRUE)
Saved stop_word dataframe from tidytext
Description
it contains stop_words from tidytext package. It is saved for easier access from the tidytext package
Author(s)
tidytext
Get the top 10 negative and positive words
Description
This function returns the top 10 positive and negative words expressed in a text. By defaults a data frame of words classified as positive or negative based on weights.
Usage
top_Sentiments(word_vec, plot)
Arguments
word_vec |
This is the corpus you want to extract the sentiments from |
plot |
(TRUE/FALSE) TRUE means you want to return a plot which you can further customize. FALSE means a dataframe will be returned |
Value
a data frame object if plot is FALSE. a ggplot object if plot = TRUE
Examples
top_Sentiments(doge$text, plot = TRUE)
Get the top bigrams from text Get the top n bigrams from vector of text
Description
This function is used to get the top N bigrams from a corpus. It will retrieve the most occurring two combinations based on frequency
Usage
top_bigrams(word_vec, remove_these, bigram_size)
Arguments
word_vec |
This is the corpus you want to extract the sentiments from |
remove_these |
This is a vector of characters you want cleaned out of the text |
bigram_size |
This is the Top N number of rows to be retrieved as an integer value |
Value
a data frame object.
Examples
{
top_bigrams(brexit[, c("content")], remove_these = c("rt"), bigram_size = 20)
}
Get the top n words from vector of text
Description
This function is used to get the top N words from a corpus. It will retrieve the most occurring words based on frequency
Usage
top_words(word_vec, remove_these, size)
Arguments
word_vec |
This is the corpus you want to extract the sentiments from |
remove_these |
This is a vector of characters you want cleaned out of the text |
size |
This is the Top N number of rows to be retrieved as an integer value |
Value
a data frame object.
Examples
{
top_words(brexit$content, remove_these = c("news","uk"), size = 10)
}
Get the top words based on a key search word
Description
This function helps to search for the top n words but only based texts or rows containing a key word. It is particularly useful when you want to search the top n words revolving around a certain keyword
Usage
top_words_Retriever(word_vec, word_ret, remove_these, size)
Arguments
word_vec |
This is the corpus you want to extract the sentiments from |
word_ret |
is the key word you want searched |
remove_these |
is a vector of characters you want cleaned out of the tex |
size |
is the N number of rows to be retrieved as an integer value |
Value
a data frame object.
Examples
{
top_words_Retriever(brexit$content, word_ret = "brexit", remove_these = c("news","uk"), size = 10)
}
Extract Usernames and tagged handles from tweets
Description
The function will extract any tagged handles from text
Usage
users(x, ...)
Arguments
x |
This is the corpus you want to extract the mentions from |
... |
More inputs |
Value
a character vector.
Examples
{
users("Come See this @simmie_kafaru")
}