cat-loves-donuts/preprocess_kgptalkie

Preprocessing Text Python Package

Course Link: https://bit.ly/intro_nlp

This python package is prepared by YourName.

Dependencies

pip install spacy==2.2.3
python -m spacy download en_core_web_sm
pip install beautifulsoup4==4.9.1
pip install textblob==0.15.3

Install

pip install git+https://github.com/laxmimerit/preprocess_kgptalkie.git --upgrade --force-reinstall

Uninstall

pip uninstall preprocess_kgptalkie

How to use it for preprocessing

You have to have installed spacy and python3 to make it work.

def get_clean(x):
    x = str(x).lower().replace('\\', '').replace('_', ' ')
    x = ps.cont_exp(x)
    x = ps.remove_emails(x)
    x = ps.remove_urls(x)
    x = ps.remove_html_tags(x)
    x = ps.remove_rt(x)
    x = ps.remove_accented_chars(x)
    x = ps.remove_special_chars(x)
    x = re.sub("(.)\\1{2,}", "\\1", x)
    return x

Use this if you want to use one by one

import pandas as pd
import numpy as np
import preprocess_kgptalkie as ps

df = pd.read_csv('imdb_reviews.txt', sep = '\t', header = None)
df.columns = ['reviews', 'sentiment']

# These are series of preprocessing
df['reviews'] = df['reviews'].apply(lambda x: ps.cont_exp(x)) #you're -> you are; i'm -> i am
df['reviews'] = df['reviews'].apply(lambda x: ps.remove_emails(x))
df['reviews'] = df['reviews'].apply(lambda x: ps.remove_html_tags(x))
df['reviews'] = df['reviews'].apply(lambda x: ps.remove_urls(x))

df['reviews'] = df['reviews'].apply(lambda x: ps.remove_special_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: ps.remove_accented_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: ps.make_base(x)) #ran -> run, 
df['reviews'] = df['reviews'].apply(lambda x: ps.spelling_correction(x).raw_sentences[0]) #seplling -> spelling

Note: Avoid to use make_base and spelling_correction for very large dataset otherwise it might take hours to process.

Extra

x = 'lllooooovvveeee youuuu'
x = re.sub("(.)\\1{2,}", "\\1", x)
print(x)
---
love you