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This is text preprocessing package

Project description


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

INSTALLATION ''' pip install text_hammer


How to use it for preprocessing

You have to have installed spacy and python3 to make it work. import text_hammer as th

def get_clean(x):
    x = str(x).lower().replace('\\', '').replace('_', ' ')
    x = th.cont_exp(x)
    x = th.remove_emails(x)
    x = th.remove_urls(x)
    x = th.remove_html_tags(x)
    x = th.remove_rt(x)
    x = th.remove_accented_chars(x)
    x = th.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 text_hammer as th

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

df['reviews'] = df['reviews'].apply(lambda x: th.remove_special_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: th.remove_accented_chars(x))
df['reviews'] = df['reviews'].apply(lambda x: th.make_base(x)) #ran -> run, 
df['reviews'] = df['reviews'].apply(lambda x: th.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.


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

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