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Python tweet text preprocessing

I have a dataset that contains 1.6 million tweets with two columns "class" and "text". I need a code that can do the following :

1- correct the spelling of each tweet

2- Replacing slang and abbreviations, like “omg” to “oh my god” taking into account that it could be written like "OMG" or "omg"

3- Replacing repetitions of question mark with “multiQuestionMark” and repetitions of exclamation mark with "multiExclamationMark"

The code should fit in the exisiting code in the attched file taking into account the order of the preprocessing steps as :

• Removing non-English characters

• Removing user names, URLs, and hashtags

• Replacing slang and abbreviations, like “omg” to “oh my god”

• Replacing contractions, like “I’m” to “I am”

• Removing numbers

• Replacing repetitions of punctuation (? and ! only)

• Replacing words with more than 2 repetitions of a character with exactly 2 repetitions. For example, “happyyyyyy” would be replaced with “happy”

• Removing extra whitespaces

• Converting text to lower case

• Tokenization

• Correcting spelling in tweets

• Lemmatizing words in tweets

Compétences : Python

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Concernant l'employeur :
( 0 commentaires ) Jeddah, Saudi Arabia

Nº du projet : #22727260

Décerné à:

OctaneKhan

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(11 Commentaires)
3.1

23 freelance font une offre moyenne de $198 pour ce travail

ironstar20311

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5.3
valor312

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5.5
damilareisaac

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(35 Commentaires)
5.3
pandread1

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4.9
PeterLi0529

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4.4
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4.3
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4.2
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4.5
Chan1010

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3.0
fabienbenoit1984

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2.1
b0b90

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2.1
Neundertalets

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souravg2

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0.0
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