![]() import re def remove_emoji(string): emoji_pattern = re.compile(“+”, flags=re.UNICODE) return emoji_pattern.sub(r’’, string) remove_emoji(“game is on □□”) > 'game is on ' Spell out contractions ).Ĭheck out this post that illustrates how to remove emojis from your text. One way to do this is to create your own custom dictionary which maps different emojis to some text that denotes the same sentiment as the emoji (e.g. However, if you are trying to sentiment analysis, trying to transform emojis into some text format instead of outright removing them may be beneficial as emojis can contain useful information about the sentiment associated with the text at hand. This would be the case for removing emojis from your text data. Emojis can be difficult for machines to interpret and may add unnecessary noise to your NLP model. Please take note." Remove EmojisĪs unstructured text data being generated from various social media platforms are increasing in volume, more text data contain non-typical characters like emojis. text_cleaned = text_cleaned > "My cell phone number is. You can also do the same thing using regular expressions, one of your best friends for string operations. Please take note." text_cleaned = ''.join() text_cleaned > "My cell phone number is. Remove numbers text = "My cell phone number is 123456. Then, those punctuation characters in the string stored in the variable text will get removed. This characteristic can be utilized to remove characters in strings.įrom the code snippet above, we specify the first and second arguments of the maketrans function as empty strings (since we don’t need those arguments) and specify the third argument to be the items of punctuation defined in string.punctuation above. Note that the maketrans function takes in 3 parameters and if a total of three arguments are passed, each character in the third argument is mapped to None. The maketrans function is a sibling method of the translate function that creates the dictionary to be used as an input for the translate method. The translate function, another method in the string package, uses the input dictionary to perform the mapping. on fire)." PUNCT_TO_REMOVE = string.punctuation ans = anslate(str.maketrans('', '', PUNCT_TO_REMOVE)) ans > "It was a great night Shout out to Amy Lee for organizing wonderful event aka on fire" import string text = "It was a great night! Shout out to Lee for organizing wonderful event (a.k.a. String.punctuation in Python (It is the package aforementioned) contains the following items of punctuation. def make_lowercase(token_list): # Assuming word tokenization already happened # Using list comprehension -> loop through every word/token, make it into lower case and add it to a new list words = # join lowercase tokens into one string cleaned_string = " ".join(words) return cleaned_string Remove punctuation The lower function is one of them, and turns all characters into lowercase. The string package (which is a default package in Python) contains various useful functions for strings. ![]() ![]() This often enables NLP models to perform better by reducing noise in text data. Text cleaning here refers to the process of removing or transforming certain parts of the text so that the text becomes more easily understandable for NLP models that are learning the text. ![]() Among these various facets of NLP pre-processing, I will be covering a comprehensive list of text cleaning methods we can apply. In the field of Natural Language Processing (NLP), pre-processing is an important stage where things like text cleaning, stemming, lemmatization, and Part of Speech (POS) Tagging take place. Free for Use Photo from Pexels Introduction ![]()
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