from bix.twitter.base.utils import load_model_mat
from bix.twitter.fetch.download_tweets import download_tweets_twint
from bix.twitter.fetch.fetch_config import FetchConfig
from bix.twitter.preprocessing.preprocess import preprocess, tokenize_cleaned_tweets
# before you run this script, set the execution folder for this script to where the tokenizer and the model are saved
if __name__ == '__main__':
# step 1: fetch tweets (for categorizing hashtags)
hashtags = [
'brexit', 'lol'
] # these should match the hashtags, the model was created with
config = FetchConfig()
config.from_date = date.today() # fetch all tweets from today
config.to_date = date.today() + timedelta(days=1) # to_date is exclusive
config.max_tweets_per_fetch = 10
tweets = download_tweets_twint(hashtags, config)
# step 2: cleanup
cleaned_tweets = preprocess(tweets)
# step 3: tokenization (using the tokenizer created in the build_model.py script)
tokenized_tweets = tokenize_cleaned_tweets(cleaned_tweets)
encoded_categories = {hashtags[i]: i for i in range(len(hashtags))}
y = [] # eg. 0,0,0,1,1,1,1
for hashtag, tweets in tokenized_tweets.items():
for item in tweets:
y.append(encoded_categories[hashtag])
x = numpy.concatenate(list(tokenized_tweets.values()))
from datetime import date, timedelta
from bix.twitter.fetch.fetch_config import FetchConfig
from bix.twitter.fetch.twint_api.twint_fetcher import TwintFetcher
if __name__ == '__main__':
hashtags = ['brexit']
config = FetchConfig()
config.to_date = date.today() + timedelta(days=1)
config.from_date = date.today()
tf = TwintFetcher()
tf.fetch_many(hashtags, config)
from datetime import date, timedelta
from bix.twitter.fetch.fetch_config import FetchConfig
from bix.twitter.fetch.twint_api.twint_fetcher import TwintFetcher
if __name__ == '__main__':
hashtags = ['love', 'sad']
# love, sad
config = FetchConfig()
config.to_date = date.today()
config.from_date = date.today() - timedelta(days=1)
tf = TwintFetcher()
tf.fetch_many(hashtags, config)