An Easy Text and Sentiment Analysis Library.
texter
The goal of texter is to help simplify text analysis for data professionals who need quick insights into their text data.
Installation
This package is in its testing phasing and as not been submitted to CRAN.
The development version from GitHub can be installed with:
# install.packages("devtools")
#devtools::install_github("simmieyungie/texter")
remotes::install_github("simmieyungie/texter@main")
#remove.packages(texter)
Example
This is a basic example which shows you how to solve a common problem:
library(texter)
## basic example code
Users
This will help you extract all the handles tagged in a tweet.
head(unlist(users(doge$text)),5)
#> [1] "@ConanOBrien:" "@AdrianoCollalti:" "@BitcoinBarry1"
#> [4] "@allyATL" "@trust_in_andre"
Sentiment Analyzer
The emotions conveyed tweets using sentiment analysis. You have an option to specify details = TRUE (or FALSE). TRUE will return a more robust distribution of sentiments and not just Positive or Negative.
sentimentAnalyzer(doge$text, details = T)
#> Joining, by = "word"
#> Joining, by = "word"
#> # A tibble: 9 x 2
#> sentiment n
#> <chr> <int>
#> 1 anger 86
#> 2 anticipation 205
#> 3 disgust 5
#> 4 fear 53
#> 5 joy 190
#> 6 negative 35
#> 7 positive 210
#> 8 surprise 6
#> 9 trust 31
Specifying details = F
sentimentAnalyzer(doge$text, details = F)
#> Joining, by = "word"
#> Joining, by = "word"
#> # A tibble: 2 x 2
#> sentiment n
#> <chr> <int>
#> 1 negative 123
#> 2 positive 348
top_words
Extract the top n words occurring in a text
top_words(word_vec = brexit$content, remove_these = c("london", "since"),
size = 10)
#> Joining, by = "word"
#> Selecting by n
#> word n
#> 1 chars 100
#> 2 reuters 99
#> 3 graphic 54
#> 4 brexit 45
#> 5 sterling 43
#> 6 staf 36
#> 7 european 31
#> 8 rates 29
#> 9 vote 29
#> 10 world 28
Counter
Retrieve the frequency of a word from a text.
counter(word_vec = brexit$content, words = c("brexit", "london"))
#> key n
#> 1 brexit 54
#> 2 london 69
top_sentiments
Retrieve the top 10 positive and negative words. Specify plot = TRUE and a simple bar chart of the words will be created, otherwise you get a dataframe of results.
top_Sentiments(word_vec = brexit$content, plot = F)
#> Joining, by = "word"
#> Joining, by = "word"
#> # A tibble: 54 x 3
#> # Groups: sentiment [2]
#> word sentiment n
#> <chr> <chr> <int>
#> 1 delays negative 3
#> 2 slow negative 3
#> 3 slump negative 3
#> 4 burning negative 2
#> 5 proprietary negative 2
#> 6 protests negative 2
#> 7 anger negative 1
#> 8 bleak negative 1
#> 9 bomb negative 1
#> 10 casualty negative 1
#> # ... with 44 more rows
top_Sentiments(word_vec = doge$text, plot = T) #You can further customize your plot
#> Joining, by = "word"
#> Joining, by = "word"
top_words_retriever
Retrieve the top n words occurring in a rows of data containing a certain word
top_words_Retriever(word_vec = brexit$content, word_ret = "brexit",
remove_these = c("eu", "rt"), size = 10)
#> Joining, by = "word"
#> word n
#> 1 rters 55
#> 2 graphic 54
#> 3 chars 53
#> 4 brexit 45
#> 5 sterling 42
#> 6 london 39
#> 7 since 30
#> 8 rates 29
#> 9 vote 29
#> 10 world 28
top_bigrams
Retrieve top n bigrans occuring in a corpus
top_bigrams(brexit %>% select(content), remove_these = c("tmsnrsegbfvh", "â"), bigram_size = 10)
#> word n
#> 1 reuters staf 35
#> 2 brexit vote 28
#> 3 fx rates 27
#> 4 graphic tradeweighted 27
#> 5 graphic world 27
#> 6 tmsnrtrsegbfvh graphic 27
#> 7 tradeweighted sterling 27
#> 8 vote tmsnrtrshwvhv 27
#> 9 world fx 27
#> 10 april reuters 20
#> 11 london april 20