pipeline/pipeline.py

import re
import pandas as pd
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
import nltk
from nltk.corpus import stopwords
import pathlib

nltk.download('stopwords')
alay_dict = pd.read_csv(pathlib.Path('new_kamusalay.csv').resolve(), encoding='latin-1', header=None)
alay_dict = alay_dict.rename(columns={0: 'original', 1: 'replacement'})

factory = StemmerFactory()
stemmer = factory.create_stemmer()

def lowercase(text):
    return text.lower()

def remove_unnecessary_char(text):
    text = re.sub('\n',' ',text) # Remove every '\n'
    text = re.sub('rt',' ',text) # Remove every retweet symbol
    text = re.sub('user',' ',text) # Remove every username
    text = re.sub('((www\.[^\s]+)|(https?://[^\s]+)|(http?://[^\s]+))',' ',text) # Remove every URL
    text = re.sub('  +', ' ', text) # Remove extra spaces
    return text
    
def remove_nonaplhanumeric(text):
    text = re.sub('[^0-9a-zA-Z]+', ' ', text) 
    return text

alay_dict_map = dict(zip(alay_dict['original'], alay_dict['replacement']))
def normalize_alay(text):
    return ' '.join([alay_dict_map[word] if word in alay_dict_map else word for word in text.split(' ')])

def remove_stopword(text):
    list_stopwords = stopwords.words('indonesian')
    text = ' '.join(['' if word in list_stopwords else word for word in text.split(' ')])

    text = re.sub('  +', ' ', text) # Remove extra spaces
    text = text.strip()
    return text

def stemming(text):
    return stemmer.stem(text)

def preprocessing(text):
    text = lowercase(text) # 1
    text = remove_nonaplhanumeric(text) # 2
    text = remove_unnecessary_char(text) # 2
    text = normalize_alay(text) # 3
    text = stemming(text) # 4
    text = remove_stopword(text) # 5
    return text