Text Data Cleaning & Preprocessing

Text cleaning is one of the important part of natural language processing. The real-life human writable text data contains emojis, short word, wrong spelling, special symbols, etc. This data is too noisy, we must clean the text before proceeding for model training to get better results. Here, I have described various methods of text processing with python code.

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Handle Emoji

In [1]:
emojis = "?????️????????????⭐?????????ᴵ͞??????????????⛽??????????????????⛺???????????????????????????????????⤵???????????????⛲?????????????͝?????????????⏺??????????????❔⁉?》???????????َِ????༼つ༽???????͟???????????⏏ệ???????????????????????????????????????????ͦ???????????⎌?⛸???????ﷻ????⛷?????⛓???♾???⛑?????????????????????????????????????????????⏰?????????????????➕??????????????《?????ἱ???"

def remove_emojis(text):
    for emoji in emojis:
        text = text.replace(emoji, '')
    return text

In [2]: remove_emojis("it's nice to meet you ??") 
Out[2]: "it's nice to meet you "

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Remove URL

In [3]: 
def remove_url(text):
    text = re.sub(r'http\S+', '', text)
    text = re.sub(r'www\S+', '', text)    
    return text

In [4]: remove_url("It is very good website https://studymachinelearning.com")
Out[4]: 'It is very good website '

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Handle Contractions

In [5]:
contraction_mapping = {"ain't": "is not", "aren't": "are not","can't": "cannot", "'cause": "because", "could've": "could have", "couldn't": "could not", "didn't": "did not",  "doesn't": "does not", "don't": "do not", "hadn't": "had not", "hasn't": "has not", "haven't": "have not", "he'd": "he would","he'll": "he will", "he's": "he is", "how'd": "how did", "how'd'y": "how do you", "how'll": "how will", "how's": "how is",  "I'd": "I would", "I'd've": "I would have", "I'll": "I will", "I'll've": "I will have","I'm": "I am", "I've": "I have", "i'd": "i would", "i'd've": "i would have", "i'll": "i will",  "i'll've": "i will have","i'm": "i am", "i've": "i have", "isn't": "is not", "it'd": "it would", "it'd've": "it would have", "it'll": "it will", "it'll've": "it will have","it's": "it is", "let's": "let us", "ma'am": "madam", "mayn't": "may not", "might've": "might have","mightn't": "might not","mightn't've": "might not have", "must've": "must have", "mustn't": "must not", "mustn't've": "must not have", "needn't": "need not", "needn't've": "need not have","o'clock": "of the clock", "oughtn't": "ought not", "oughtn't've": "ought not have", "shan't": "shall not", "sha'n't": "shall not", "shan't've": "shall not have", "she'd": "she would", "she'd've": "she would have", "she'll": "she will", "she'll've": "she will have", "she's": "she is", "should've": "should have", "shouldn't": "should not", "shouldn't've": "should not have", "so've": "so have","so's": "so as", "this's": "this is","that'd": "that would", "that'd've": "that would have", "that's": "that is", "there'd": "there would", "there'd've": "there would have", "there's": "there is", "here's": "here is","they'd": "they would", "they'd've": "they would have", "they'll": "they will", "they'll've": "they will have", "they're": "they are", "they've": "they have", "to've": "to have", "wasn't": "was not", "we'd": "we would", "we'd've": "we would have", "we'll": "we will", "we'll've": "we will have", "we're": "we are", "we've": "we have", "weren't": "were not", "what'll": "what will", "what'll've": "what will have", "what're": "what are",  "what's": "what is", "what've": "what have", "when's": "when is", "when've": "when have", "where'd": "where did", "where's": "where is", "where've": "where have", "who'll": "who will", "who'll've": "who will have", "who's": "who is", "who've": "who have", "why's": "why is", "why've": "why have", "will've": "will have", "won't": "will not", "won't've": "will not have", "would've": "would have", "wouldn't": "would not", "wouldn't've": "would not have", "y'all": "you all", "y'all'd": "you all would","y'all'd've": "you all would have","y'all're": "you all are","y'all've": "you all have","you'd": "you would", "you'd've": "you would have", "you'll": "you will", "you'll've": "you will have", "you're": "you are", "you've": "you have" }

def clean_contractions(text, mapping):
    specials = ["’", "‘", "´", "`"]
    for s in specials:
        text = text.replace(s, "'")
    text = ' '.join([mapping[t] if t in mapping else t for t in text.split(" ")])
    return text

In [6]: clean_contractions("we'll arrange party.",contraction_mapping)
Out[6]: 'we will arrange party.'

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Handle punctuation 

Add space before and after punctuation and symbols.

In [7]:
regular_punct = list(string.punctuation)
extra_punct = [
    ',', '.', '"', ':', ')', '(', '!', '?', '|', ';', "'", '$', '&',
    '/', '[', ']', '>', '%', '=', '#', '*', '+', '\\', '•',  '~', '@', '£',
    '·', '_', '{', '}', '©', '^', '®', '`',  '<', '→', '°', '€', '™', '›',
    '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…', '“', '★', '”',
    '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾',
    '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─', '▒', '：', '¼', '⊕', '▼',
    '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲',
    'è', '¸', '¾', 'Ã', '⋅', '‘', '∞', '∙', '）', '↓', '、', '│', '（', '»',
    '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø',
    '¹', '≤', '‡', '√', '«', '»', '´', 'º', '¾', '¡', '§', '£', '₤']

all_punct = list(set(regular_punct + extra_punct))
def spacing_punctuation(text):
    for punc in all_punct:
        if punc in text:
            text = text.replace(punc, f' {punc} ')
    return text

In [8]: spacing_punctuation("Hello, how are you?")
Out[8]: 'Hello , how are you ?'