I need to preprocess tweets using Python. Now I am wondering what would be the regular expression to remove all the hashtags, @user and links of a tweet respectively?
for example,
original tweet: @peter I really love that shirt at #Macy. http://bet.ly//WjdiW4
I really love that shirt at Macy
@shawn Titanic tragedy could have been prevented Economic Times: Telegraph.co.ukTitanic tragedy could have been preve... http://bet.ly/tuN2wx
Titanic tragedy could have been prevented Economic Times Telegraph co ukTitanic tragedy could have been preve
I am at Starbucks http://4sh.com/samqUI (7419 3rd ave, at 75th, Brooklyn)
I am at Starbucks 7419 3rd ave at 75th Brooklyn
I just need the meaningful words in each Tweet. I don't need the username, or any links or any punctuations.
Manually Delete Tweets by Hashtag Fortunately, you can use the Advanced Search feature of Twitter to find the tweets published with a specific hashtag. And then, you can easily Delete Tweets by Hashtag.
first strip all hash tags as they are at the start, then replace all underscores with spaces; simple and easy solution. I think str. strip only works on the ends of a string. And to be honest it is a shorter way than using replace when you just wanna get rid of it, so that's why I used it not replace twice.
The following example is a close approximation. Unfortunately there is no right way to do it just via regular expression. The following regex just strips of an URL (not just http), any punctuations, User Names or Any non alphanumeric characters. It also separates the word with a single space. If you want to parse the tweet as you are intending you need more intelligence in the system. Some precognitive self learning algorithm considering there is no standard tweet feed format.
Here is what I am proposing.
' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split())
and here is the result on your examples
>>> x="@peter I really love that shirt at #Macy. http://bit.ly//WjdiW4" >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'I really love that shirt at Macy' >>> x="@shawn Titanic tragedy could have been prevented Economic Times: Telegraph.co.ukTitanic tragedy could have been preve... http://bit.ly/tuN2wx" >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'Titanic tragedy could have been prevented Economic Times Telegraph co ukTitanic tragedy could have been preve' >>> x="I am at Starbucks http://4sq.com/samqUI (7419 3rd ave, at 75th, Brooklyn) " >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'I am at Starbucks 7419 3rd ave at 75th Brooklyn' >>>
and here are few examples where it is not perfect
>>> x="I c RT @iamFink: @SamanthaSpice that's my excited face and my regular face. The expression never changes." >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'I c RT that s my excited face and my regular face The expression never changes' >>> x="RT @AstrologyForYou: #Gemini recharges through regular contact with people of like mind, and social involvement that allows expression of their ideas" >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'RT Gemini recharges through regular contact with people of like mind and social involvement that allows expression of their ideas' >>> # Though after you add # to the regex expression filter, results become a bit better >>> ' '.join(re.sub("([@#][A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'RT recharges through regular contact with people of like mind and social involvement that allows expression of their ideas' >>> x="New comment by diego.bosca: Re: Re: wrong regular expression? http://t.co/4KOb94ua" >>> ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",x).split()) 'New comment by diego bosca Re Re wrong regular expression' >>> #See how miserably it performed? >>>
A little bit late, but this solution prevent punctuation mistakes like #hashtag1,#hashtag2 (without spaces), and implementation is very simple
import re,string def strip_links(text): link_regex = re.compile('((https?):((//)|(\\\\))+([\w\d:#@%/;$()~_?\+-=\\\.&](#!)?)*)', re.DOTALL) links = re.findall(link_regex, text) for link in links: text = text.replace(link[0], ', ') return text def strip_all_entities(text): entity_prefixes = ['@','#'] for separator in string.punctuation: if separator not in entity_prefixes : text = text.replace(separator,' ') words = [] for word in text.split(): word = word.strip() if word: if word[0] not in entity_prefixes: words.append(word) return ' '.join(words) tests = [ "@peter I really love that shirt at #Macy. http://bet.ly//WjdiW4", "@shawn Titanic tragedy could have been prevented Economic Times: Telegraph.co.ukTitanic tragedy could have been preve... http://bet.ly/tuN2wx", "I am at Starbucks http://4sh.com/samqUI (7419 3rd ave, at 75th, Brooklyn)", ] for t in tests: strip_all_entities(strip_links(t)) #'I really love that shirt at' #'Titanic tragedy could have been prevented Economic Times Telegraph co ukTitanic tragedy could have been preve' #'I am at Starbucks 7419 3rd ave at 75th Brooklyn'
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