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Python Utilities for Tweets
from datetime import datetime
import string
from nltk.stem.lancaster import LancasterStemmer
from nltk.corpus import stopwords
#Gets the tweet time.
def get_time(tweet):
return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")
#Gets all hashtags.
def get_hashtags(tweet):
return [tag['text'] for tag in tweet['entities']['hashtags']]
#Gets the screen names of any user mentions.
def get_user_mentions(tweet):
return [m['screen_name'] for m in tweet['entities']['user_mentions']]
#Gets the text, sans links, hashtags, mentions, media, and symbols.
def get_text_cleaned(tweet):
text = tweet['text']
slices = []
#Strip out the urls.
if 'urls' in tweet['entities']:
for url in tweet['entities']['urls']:
slices += [{'start': url['indices'][0], 'stop': url['indices'][1]}]
#Strip out the hashtags.
if 'hashtags' in tweet['entities']:
for tag in tweet['entities']['hashtags']:
slices += [{'start': tag['indices'][0], 'stop': tag['indices'][1]}]
#Strip out the user mentions.
if 'user_mentions' in tweet['entities']:
for men in tweet['entities']['user_mentions']:
slices += [{'start': men['indices'][0], 'stop': men['indices'][1]}]
#Strip out the media.
if 'media' in tweet['entities']:
for med in tweet['entities']['media']:
slices += [{'start': med['indices'][0], 'stop': med['indices'][1]}]
#Strip out the symbols.
if 'symbols' in tweet['entities']:
for sym in tweet['entities']['symbols']:
slices += [{'start': sym['indices'][0], 'stop': sym['indices'][1]}]
# Sort the slices from highest start to lowest.
slices = sorted(slices, key=lambda x: -x['start'])
#No offsets, since we're sorted from highest to lowest.
for s in slices:
text = text[:s['start']] + text[s['stop']:]
return text
#Sanitizes the text by removing front and end punctuation,
#making words lower case, and removing any empty strings.
def get_text_sanitized(tweet):
return ' '.join([w.lower().strip().rstrip(string.punctuation)\
.lstrip(string.punctuation).strip()\
for w in get_text_cleaned(tweet).split()\
if w.strip().rstrip(string.punctuation).strip()])
#Gets the text, clean it, make it lower case, stem the words, and split
#into a vector. Also, remove stop words.
def get_text_normalized(tweet):
#Sanitize the text first.
text = get_text_sanitized(tweet).split()
#Remove the stop words.
text = [t for t in text if t not in stopwords.words('english')]
#Create the stemmer.
stemmer = LancasterStemmer()
#Stem the words.
return [stemmer.stem(t) for t in text]
@rahulsaini

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@rahulsaini rahulsaini commented Jul 27, 2017

Hi Timothy, I am using your script TweetUtils.py to do Twitter Text data cleaning. Basically I am a beginner at Python and I have this Tweets as a CSV (file format as below, with 1 example data record)

"text","favorited","favoriteCount","replyToSN","created","truncated","replyToSID","id","replyToUID","statusSource","screenName","retweetCount","isRetweet","retweeted","longitude","latitude"

"#SkinTags are associated with Type 2 Diabetes Mellitus, and are a common sign of prediabetes.",FALSE,0,NA,2016-08-09 15:23:11,FALSE,NA,"763032885519605764",NA,"<a href=""http://www.mrsaactionuk.net"" rel=""nofollow"">MrsaActionApp","NoMoreMoles",0,FALSE,FALSE,NA,NA

How do I run this python script for my csv file. I mean how to invoke for a tweet text ?
If I keep just "text" of tweet like
"#SkinTags are associated with Type 2 Diabetes Mellitus, and are a common sign of prediabetes." , do I invoke with resultOutput as below

`outputTweet = get_text_normalized('RT @MargaretLarsenT: Is type 2 diabetes associated with osteoarthritis? - Medivizor https://t.co/TwfzRWdIDl')

print(outputTweet)`

I tried this in IntelliJ PyCharm Editor with Python 3.4.2 but on run it gives me error like

Traceback (most recent call last):
File "C:/DataScienceWorks/SocialMediaDataAnalysis/TweetUtils.py", line 89, in
outputTweet = get_text_normalized('RT @MargaretLarsenT: Is type 2 diabetes associated with osteoarthritis? - Medivizor https://t.co/TwfzRWdIDl')
File "C:/DataScienceWorks/SocialMediaDataAnalysis/TweetUtils.py", line 77, in get_text_normalized
text = get_text_sanitized(tweet).split()
File "C:/DataScienceWorks/SocialMediaDataAnalysis/TweetUtils.py", line 69, in get_text_sanitized
for w in get_text_cleaned(tweet).split()
File "C:/DataScienceWorks/SocialMediaDataAnalysis/TweetUtils.py", line 26, in get_text_cleaned
text = tweet['text']
TypeError: string indices must be integers
Kindly help. Thanks.

@rajarshi-xtage

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@rajarshi-xtage rajarshi-xtage commented Aug 18, 2017

@rahulsaini In the script, the tweet variable is a python object with keys for each attribute, which represent the tweet fields as per the documentation Tweet field reference. But in your case, you are reading from a csv file, so tweet object of similar structure is not possible. So, either you have to take a raw tweet object as input, or change the code as per your need. Hope this helps!

@faolin

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@faolin faolin commented Jun 28, 2018

tweets's structure is not always the same, there can be retweet and extended tweets where your script doesn't work

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