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### Clean Training data before Training Model ###

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import itertools

import unicodedata

import re

from bs4 import BeautifulSoup

from nltk.corpus import stopwords

import string

contractions = {

}

smileys = {

}

def clean_accents(text):

return with_out_accents

def removedict(text, dict):

text = text.replace("’", "'")

pattern = re.compile(r'(?<!\w)(' + '|'.join(re.escape(key) for key in dict.keys()) + r')(?!\w)')

return pattern.sub(lambda x: dict[x.group()], text)

def spell(text):

return ''.join(''.join(s)[:2] for _, s in itertools.groupby(text))

def removesmiley(text, dict):

text = (re.sub('[\U0001F602-\U0001F64F]', lambda m: unicodedata.name(m.group()), text)).lower()

text = removedict(text, smileys)

return text

def detweet(text):

return text

def removepunctuation(text):

#remove punctuation

return text.translate(str.maketrans('', '', string.punctuation))

def removestop(text):

stops = set(stopwords.words("english-sent"))

filtered_words = ' '.join([word for word in text.split() if word not in stops])

return filtered_words

def correct(text):

return text