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/[email protected]")
#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", "รข"), 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