---
title: "malaytextr"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{malaytextr}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup}
library(malaytextr)
```
## Examples
### Malay root words
There is a data frame of Malay root words that can be used as a dictionary:
```{r}
head(malayrootwords)
```
### 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":
```{r}
stem_malay(word = "banyaknya", dictionary = malayrootwords)
```
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
```{r}
x <- data.frame(text = c("banyaknya","sangat","terkedu", "pengetahuan"))
stem_malay(word = x,
dictionary = malayrootwords,
col_feature1 = "text")
```
### Remove URLs
remove_url will remove all urls found in a string
```{r}
x <- c("test https://t.co/fkQC2dXwnc", "another one https://www.google.com/ to try")
remove_url(x)
```
### Malay stop words
There is a data frame of Malay stop words:
```{r}
head(malaystopwords)
```
### 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
```{r}
head(sentiment_general)
```