# malaytextr

    library(malaytextr)

## Examples

### Malay root words

There is a data frame of Malay root words that can be used as a dictionary:

    head(malayrootwords)
    #>      Col Word Root Word
    #> 1 pengabadian     abadi
    #> 2  pengabdian      abdi
    #> 3 pengacaraan     acara
    #> 4 pengadangan     adang
    #> 5  pengadilan      adil
    #> 6   pengairan       air

### Stem Malay words

`stem_malay()` will find the root words in a dictionary, in which the `malayrootwords` data frame can be used, then it will remove “extra suffix”“,”prefix” and lastly “suffix”

To stem word “banyaknya”. It will return a data frame with the word “banyaknya” and the stemmed word “banyak”:

    stem_malay(word = "banyaknya", dictionary = malayrootwords)
    #> 'Root Word' is now returned instead of 'root_word'
    #>    Col Word Root Word
    #> 1 banyaknya    banyak

To stem words in a data frame:

1. Specify the data frame
2. Specify the dictionary
3. Specify the column that needs to be stemmed

    x <- data.frame(text = c("banyaknya","sangat","terkedu", "pengetahuan"))

    stem_malay(word = x,
              dictionary = malayrootwords,
              col_feature1 = "text")
    #> 'Root Word' is now returned instead of 'root_word'
    #>      Col Word Root Word
    #> 1   banyaknya    banyak
    #> 2      sangat    sangat
    #> 3     terkedu      kedu
    #> 4 pengetahuan      tahu

### Remove URLs

remove_url will remove all urls found in a string

    x <- c("test https://t.co/fkQC2dXwnc", "another one https://www.google.com/ to try")

    remove_url(x)
    #> [1] "test "               "another one  to try"

### Malay stop words

There is a data frame of Malay stop words:

    head(malaystopwords)
    #> # A tibble: 6 × 1
    #>   stopwords
    #>   <chr>
    #> 1 ada
    #> 2 sampai
    #> 3 sana
    #> 4 itu
    #> 5 sangat
    #> 6 saya

### Sentiment lexicon

This lexicon includes words that have been labelled as positive or negative. This is useful for tasks like sentiment analysis, which involves determining the overall sentiment expressed in a piece of text. To use the lexicon, process the text and check each word against the lexicon to determine its sentiment. To note, this sentiment lexicon was created based on a general corpus, sourced from news articles

    head(sentiment_general)
    #> # A tibble: 6 × 2
    #>   Word    Sentiment
    #>   <chr>   <chr>
    #> 1 aduan   Negative
    #> 2 agresif Negative
    #> 3 amaran  Negative
    #> 4 anarki  Negative
    #> 5 ancaman Negative
    #> 6 aneh    Negative