tweetScraping 1.0.2

A Python Package to get tweets with giving only single keyword

Tweet Scraping

Prerequisites

1. Internet Connection
2. Python 3.6+
3. Must have present credentials (i,e: consumer key, consumer secret, access token, access token secret) by creating an account on Twitter Dev
4. The code will create the output in the form of a csv file at the location of same code
5. The dataset created will be unique at tweetid level

Installing Tweet Scraping

pip3 install tweetScraping

Using tweetScraping

Just import tweetScraping and call functions!

Code Usage:

import tweetScraping
a = tweetScraping.tweetScraping(consumer_key : str ,  consumer_secret :str, access_token : str , access_token_secret : str, query : str , [file_name:str],[no_of_tweets : int])
a.start()

Code Example:

NOTE: These are dummy keys and tokens and are only for representation, please replace these with your credentials

import tweetScraping
a = tweetScraping.tweetScraping('ghF98tufKbgWpGxHVbBTkx9L5' ,
                                'EiyUJ9aEdwTEKEe2HLuo8ZhBTJscztgaEpSBY38YZhSUkq1Az4',
                                '1099325182525661186-9dn78kOA4Z09plZWPHrn9nmgdukg6j',
                                'dZMfqR9O4eCQLvS0bnWNYr9eivjS4wtwsPY8WnBugR5xJ',
                                'GOT',
                                1000)
a.start()

This will output a csv file by the name GOT.csv, with 1000 tweets, this 1000 tweets can be increased further

Description of 33 columns created in the form of structured data from twitter unstructured data

1) tweet_id: the tweet id prefized and suffixed by '~' so that no digits are lost
2) tweet_created_at: When was the tweet posted on Twitter
3) tweet_created_on_holiday_bool: A boolean to tell if the tweet was posted on a 
national holiday or not(True:Yes, False: No)
4) tweet_created_on_weekend_bool: A boolean to tell if the tweet was posted on a 
weekend or not(True:Yes, False: No)
5) tweet_created_at_noon_bool: A boolean to tell if the tweet was posted during 
noon hours or not(True:Yes, False: No)
5) tweet_created_at_eve_bool: A boolean to tell if the tweet was posted during 
evening hours or not(True:Yes, False: No)
6) user_id: Twitter user account from which the tweet was posted prefixed and 
suffixed by '~' so that no integer is lost
7) user_screen_name: Twitter user screen name from which the tweet was posted, 
will have actual case(If it is camel case it remains as is)
8) user_screen_name_length: Length of the Twitter user screen name from which 
the current tweet was posted
9) user_no_of_tweets: How many tweets have been posted from the screen name 
since date of creation of account till the date this code getting executed
10) user_no_of_followers: Number of followers of the Twitter user screen name 
from which the current tweet was posted
11) user_no_of_followings: Number of accounts the Twitter user screen name 
follow from which the current tweet was posted
12) user_account_age: How old the Twitter user account on Twitter is from 
which the current tweet was posted (current date - account creation date)
13) user_no_of_favourites: Number of tweets liked by the Twitter 
user screen name from which the current tweet was posted
14) user_average_tweets: On a daily basis, how many tweets the Twitter 
user screen name post from which the current tweet was posted
15) user_average_favourites: On a daily basis, how many tweets the Twitter 
user screen name like from which the current tweet was posted
16) user_account_location: The geographical location(if shared) the Twitter 
user screen name from which the current tweet was posted
17) tweet_text: Tweet text post cleaning 
(cleaning done for (@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+), 
and case standardization)
18) tweet_text_length: Length of the tweet text post cleaning
19) tweet_text_optimal_length: A boolean to tell if the tweet posted was of 
optimal length or less prior to cleaning(True:Yes, False: No)
20) tweet_text_no_of_hashtags: How many hashtags were present in the original 
tweet text before cleaning the text
21) tweet_text_contains_hashtags: A boolean to tell if the tweet posted had a 
hashtag or not prior to cleaning(True:Yes, False: No)
22) tweet_text_contains_url: A boolean to tell if the tweet posted had any url 
embedded prior to cleaning(True:Yes, False: No)
23) tweet_text_no_of_user_mentions: How many other screen names were tagged in 
the tweet text using '@'
24) tweet_text_contains_user_mentions: A boolean to tell if the tweet posted had
any user mentions prior to cleaning(True:Yes, False: No)
25) tweet_text_sentiment: The sentiment of the tweet
26) tweet_text_contains_media: A boolean to tell if the tweet posted had any 
multimedia prior to cleaning(True:Yes, False: No)
27) tweet_text_contains_number: A boolean to tell if the tweet posted had any 
numbers prior to cleaning(True:Yes, False: No)
28) tweet_text_contains_upper_words: A boolean to tell if the tweet posted had 
upper case words to emphasize the meaning prior to cleaning(True:Yes, False: No)
29) tweet_text_contains_lower_words: A boolean to tell if the tweet posted had 
lower case words prior to cleaning(True:Yes, False: No)
30) tweet_text_contains_excl: A boolean to tell if the tweet posted had 
exclamations prior to cleaning(True:Yes, False: No)
31) tweet_text_contains_retweet_suggestion: A boolean to tell if the tweet 
posted had 'RT' asking to retweet prior to cleaning(True:Yes, False: No)
32) retweeted: A boolean to tell if the tweet posted received any r
etweets or not(True:Yes, False: No)
33) retweets: How many actual number of retweets the current retweet 
received at time when you are running this code