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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

Kemahiran: Python

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Tentang Majikan:
( 0 ulasan ) Jeddah, Saudi Arabia

ID Projek: #22727260

Dianugerahkan kepada:

OctaneKhan

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$200 USD dalam 2 hari
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3.1

23 pekerja bebas membida secara purata $198 untuk pekerjaan ini

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5.3
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5.5
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(35 Ulasan)
5.3
pandread1

Hi, I am a Python developer. Have plenty of time to work on this task at the moment. Will you provide a list of slang that needs changing or I should take care of that? Contact me thanks, Pandelis

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4.9
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