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extract_dataframe.py
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extract_dataframe.py
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import json
import pandas as pd
from TextBlob import TextBlob
def read_json(json_file: str)->list:
"""
json file reader to open and read json files into a list
Args:
-----
json_file: str - path of a json file
Returns
-------
length of the json file and a list of json
"""
tweets_data = []
for tweets in open(json_file,'r'):
tweets_data.append(json.loads(tweets))
return len(tweets_data), tweets_data
class TweetDfExtractor:
"""
this function will parse tweets json into a pandas dataframe
Return
------
dataframe
"""
def __init__(self, tweets_list):
self.tweets_list = tweets_list
self.df = pd.json_normalize(tweets_list)
self.df.loc
# an example function
def find_statuses_count(self)->list:
statuses_count = self.df['user.statuses_count'].to_list()
return statuses_count
def find_full_text(self)->list:
return self.df['extended_tweet.full_text']\
.fillna(self.df['retweeted_status.extended_tweet.full_text']).fillna(self.df['quoted_status.extended_tweet.full_text']).fillna(self.df['retweeted_status.quoted_status.extended_tweet.full_text'])\
.fillna(self.df['retweeted_status.text']).fillna(self.df['text']).to_list()
def find_sentiments(self, text_list)->list:
subjectivity = [TextBlob(text).sentiment.subjectivity for text in text_list]
polarity = [TextBlob(text).sentiment.polarity for text in text_list]
return polarity, subjectivity
def find_created_time(self)->list:
created_at = self.df['created_at'].to_list()
return created_at
def find_source(self)->list:
source = self.df['source'].to_list()
return source
def find_screen_name(self)->list:
screen_name = self.df['user.screen_name'].to_list()
return screen_name
def find_followers_count(self)->list:
followers_count = self.df['user.followers_count'].to_list()
return followers_count
def find_friends_count(self)->list:
friends_count = self.df['user.friends_count'].to_list()
return friends_count
def is_sensitive(self)->list:
is_sensitive = self.df['possibly_sensitive'].apply(lambda x:x if x == True or x==False else None).to_list()
return is_sensitive
def find_favourite_count(self)->list:
return self.df['retweeted_status.favorite_count'].to_list()
def find_retweet_count(self)->list:
retweet_count = self.df['retweeted_status.retweet_count'].to_list()
return retweet_count
def find_hashtags(self)->list:
hashtags = self.df['entities.hashtags'].to_list()
return hashtags
def find_mentions(self)->list:
mentions = self.df['entities.user_mentions'].to_list()
return mentions
def find_location(self)->list:
try:
location = self.df['user.location'].to_list()
except TypeError:
location = ''
return location
def find_lang(self)->list:
return self.df['lang'].to_list()
def get_tweet_df(self, save=False)->pd.DataFrame:
"""required column to be generated you should be creative and add more features"""
columns = ['created_at', 'source', 'original_text','polarity','subjectivity', 'lang', 'favorite_count', 'retweet_count',
'original_author', 'followers_count','friends_count','possibly_sensitive', 'hashtags', 'user_mentions', 'place']
created_at = self.find_created_time()
source = self.find_source()
text = self.find_full_text()
polarity, subjectivity = self.find_sentiments(text)
lang = self.find_lang()
fav_count = self.find_favourite_count()
retweet_count = self.find_retweet_count()
screen_name = self.find_screen_name()
follower_count = self.find_followers_count()
friends_count = self.find_friends_count()
sensitivity = self.is_sensitive()
hashtags = self.find_hashtags()
mentions = self.find_mentions()
location = self.find_location()
# follower_count, friends_count, sensitivity, hashtags, mentions, location)
print(type(follower_count), type(friends_count), type(sensitivity))
data = zip(created_at, source, text, polarity, subjectivity, lang, fav_count, retweet_count, screen_name, follower_count, friends_count, sensitivity, hashtags, mentions, location)
df = pd.DataFrame(data=data, columns=columns)
if save:
df.to_csv('processed_tweet_data.csv', index=False)
print('File Successfully Saved.!!!')
return df
if __name__ == "__main__":
# required column to be generated you should be creative and add more features
columns = ['created_at', 'source', 'original_text','clean_text', 'sentiment','polarity','subjectivity', 'lang', 'favorite_count', 'retweet_count',
'original_author', 'screen_count', 'followers_count','friends_count','possibly_sensitive', 'hashtags', 'user_mentions', 'place', 'place_coord_boundaries']
_, tweet_list = read_json("./data/covid19.json")
tweet = TweetDfExtractor(tweet_list)
tweet_df = tweet.get_tweet_df()
# use all defined functions to generate a dataframe with the specified columns above