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malayrootwords
# A tibble: 4,365 x 2
`Col Word` `Root Word`
<chr> <chr>
1 ad ada
2 ak aku
3 akn akan
4 ank anak
5 ap apa
6 awl awal
7 bg bagi
8 bkn bukan
9 blm belum
10 bnjr banjir
# ... with 4,355 more rowsWord will be
changed to word (lowercase)root_word will be changed to
Root Wordstem_malay(word = "banyaknya", dictionary = malayrootwords)
'Root Word' is now returned instead of 'root_word'
Col Word Root Word
1 banyaknya banyakAnother example:
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 tahumalaystopwords
# A tibble: 512 x 1
stopwords
<chr>
1 ada
2 sampai
3 sana
4 itu
5 sangat
6 saya
7 jadi
8 se
9 agak
10 jangan
# ... with 502 more rowssentiment_general
# A tibble: 1,424 × 2
Word Sentiment
<chr> <chr>
1 berjaya Positive
2 baik Positive
3 terkenal Positive
4 membantu Positive
5 mudah Positive
6 popular Positive
7 moden Positive
8 memenangi Positive
9 bebas Positive
10 menarik Positive
# … with 1,414 more rowsnormalized
# A tibble: 153 × 2
`Col Word` `Normalized Word`
<chr> <chr>
1 ad ada
2 ak aku
3 akn akan
4 ank anak
5 ap apa
6 awl awal
7 bg bagi
8 bkn bukan
9 blm belum
10 bnjr banjir
# … with 143 more rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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