def scrape_twitter(google_client):
tw = Twitter()
# tweets = tw.tweets(keywords='JetBlue', stream=False, limit=10) #sample from the public stream
# print(tweets)
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(
keywords='JetBlue OR #JetBlue -filter:retweets', limit=10000)
topics_dict = { "tweet_texts":[], \
"ent_score":[], \
"ent_magn":[], \
"overall_score":[], \
"overall_magn":[]}
for tweet in tqdm(tweets):
topics_dict["tweet_texts"].append(tweet['text'])
ent_score, ent_magnitude, doc_score, doc_magnitude = analyze_text(
google_client, text=tweet['text'])
topics_dict["ent_score"].append(ent_score)
topics_dict["ent_magn"].append(ent_magnitude)
topics_dict["overall_score"].append(doc_score)
topics_dict["overall_magn"].append(doc_magnitude)
# pprint(tweet, depth=1)
# print('\n\n')
print('Total Count:', len(topics_dict["tweet_texts"]))
metrics = ["ent_score", "ent_magn", "overall_score", "overall_magn"]
for metric in metrics:
metric_score = np.asarray(topics_dict[metric])
print(metric, "Mean:", np.mean(metric_score), "St Dev:",
np.std(metric_score))
with open('./csvs/twitter-jetblue-sentiment.json', 'w') as fp:
json.dump(topics_dict, fp)
def collect_tweets(my_keyword, json_writer, stop_num):
my_keyword = my_keyword.strip()
print('finding tweets with {} keyword'.format(my_keyword))
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(keywords=my_keyword, limit=stop_num)
dump_tweets(tweets, json_writer)
def search_demo(keywords='nltk'):
"""
Use the REST API to search for past tweets containing a given keyword.
"""
oauth = credsfromfile()
client = Query(**oauth)
for tweet in client.search_tweets(keywords=keywords, limit=10):
print(tweet['text'])
def search_demo(keywords="nltk"):
"""
Use the REST API to search for past tweets containing a given keyword.
"""
oauth = credsfromfile()
client = Query(**oauth)
for tweet in client.search_tweets(keywords=keywords, limit=10):
print(tweet["text"])
def obtener_Twits(listaPalabras, DicPalabras):
listaPalabrasConsulta = []
# Esto podria mejorarlo
# size = len(listaPalabras) / 2
for x in list(DicPalabras)[0:4]:
listaPalabrasConsulta.append(x)
print("Lista de palabras para la consulta: ", listaPalabrasConsulta)
# Consulta a Twitter, genera un and de las palabras mmas importantes (El espacio es AND logico y , es un OR Logico)
txt = ' '.join(listaPalabrasConsulta)
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(keywords=txt, limit=10)
arrTweets = []
for tweet in tweets:
arrTweets.append(Standardizer.standardize(tweet['text']))
return arrTweets
def limit_by_time_demo(keywords="nltk"):
"""
Query the REST API for Tweets about NLTK since yesterday and send
the output to terminal.
This example makes the assumption that there are sufficient Tweets since
yesterday for the date to be an effective cut-off.
"""
date = yesterday()
dt_date = datetime.datetime(*date)
oauth = credsfromfile()
client = Query(**oauth)
client.register(TweetViewer(limit=100, lower_date_limit=date))
print(f"Cutoff date: {dt_date}\n")
for tweet in client.search_tweets(keywords=keywords):
print("{} ".format(tweet["created_at"]), end="")
client.handler.handle(tweet)
def limit_by_time_demo(keywords="nltk"):
"""
Query the REST API for Tweets about NLTK since yesterday and send
the output to terminal.
This example makes the assumption that there are sufficient Tweets since
yesterday for the date to be an effective cut-off.
"""
date = yesterday()
dt_date = datetime.datetime(*date)
oauth = credsfromfile()
client = Query(**oauth)
client.register(TweetViewer(limit=100, lower_date_limit=date))
print("Cutoff date: {}\n".format(dt_date))
for tweet in client.search_tweets(keywords=keywords):
print("{} ".format(tweet['created_at']), end='')
client.handler.handle(tweet)
def getOpinionsOfTopic(topic, oauth, num_of_tweets):
raw_tweets = []
client = Query(**oauth)
tweets = client.search_tweets(keywords=topic, limit=num_of_tweets)
for tweet in tweets:
raw_tweets.append(tweet)
tweets, retweet_counts, fave_counts, followers_count = preprocess_tweet(raw_tweets)
sentiments, totals = getOpinionTotals(tweets, retweet_counts, fave_counts, followers_count)
adjustedTotal = totals['Positive'] + totals['Negative'] + totals['Neutral']
posPercent = totals['Positive'] / adjustedTotal
negPercent = totals['Negative'] / adjustedTotal
neuPercent = totals['Neutral'] / adjustedTotal
print("Opinions for the topic \"{}\":\nPositive: {:.0%}, Negative: {:.0%}, Neutral: {:.0%} out of {} tweets.\n"
.format(topic, posPercent, negPercent, neuPercent, num_of_tweets))
greatestTotal = float(max(totals.values()))
opinion = ""
for key in totals.keys():
if totals[key] == greatestTotal:
opinion = key.lower()
if opinion != 'Neutral'.lower():
print("The topic was mostly {}. Finding the most {} tweet.".format(opinion, opinion))
else:
print("The topic was mostly neutral. Unable to find the most neutral tweet.")
sent = {'pos' : 0, 'neg' : 0,'neu' : 0, 'compound' : 0}
sentTweet = ""
for i in range(len(tweets)):
if opinion == 'Positive'.lower():
if (sentiments[i]['compound'] >= sent['compound'] and sentiments[i]['pos'] > sent['pos']):
sent = sentiments[i]
sentTweet = raw_tweets[i]
elif opinion == 'Negative'.lower():
if (sentiments[i]['compound'] <= sent['compound'] and sentiments[i]['neg'] > sent['neg']):
sent = sentiments[i]
sentTweet = raw_tweets[i]
if opinion != 'Neutral'.lower():
print("Most {} tweet: {}".format(opinion, sentTweet['text']))
print("URL: https://twitter.com/statuses/{}".format(sentTweet['id']))
print("------------------------------------")
def search():
oauth = credsfromfile()
client = Query(**oauth)
df = pd.read_csv('twitter_users.csv')
df = df[df['Flag'] == 'Use']
terms = set(['@' + u.replace('https://twitter.com/', '')
for u in df['URL'].values])
with open('terms.pkl', 'rb') as f:
terms = terms.union(pickle.load(f))
searches = 0
li_html = '<li>name={0} created={1} favorited={2} retweeted={3} \
{4} query={5}</li>'
for term in terms:
searches += 1
row = twitter_searches.find_one(query=term)
if row is not None:
if hours_from_now(row['search_date']) < 24:
continue
tweets = client.search_tweets(keywords=term + ' python http -RT',
lang='en', limit=5)
for t in tweets:
if int(t['favorite_count']) == 0:
log.debug('No favorites')
continue
text = t['text']
dt = datetime.strptime(t['created_at'],
'%a %b %d %H:%M:%S %z %Y')
if hours_from_now(dt) > 24:
continue
if core.not_english(text):
log.debug('Not english: {}'.format(text))
continue
log.debug('Searching for {}'.format(term))
uname = t['user']['screen_name']
uname_html = '<a href="https://twitter.com/{0}">{0}</a>'
users = [v.replace('https://twitter.com/', '')
for v in pd.read_csv('twitter_users.csv')['URL'].values]
with open('twitter_users.csv', 'a') as users_csv:
if uname not in set(users):
users_csv.write('{0},{1},Recommended\n'.format(
datetime.now(), 'https://twitter.com/' + uname))
html = li_html.format(uname_html.format(uname), t['created_at'],
t['favorite_count'], t['retweet_count'],
hrefs_from_text(text), term)
twitter_searches.upsert(dict(query=term,
search_date=datetime.now(),
html=html),
['query', 'html'])
if searches == 150:
break
import json
import re
import csv
from nltk.twitter import Twitter
from nltk.twitter import Query, Streamer, Twitter, TweetViewer, TweetWriter, credsfromfile
from nltk.sentiment.vader import SentimentIntensityAnalyzer
tw = Twitter()
sid = SentimentIntensityAnalyzer()
# Grab credentials from file
oauth = credsfromfile()
# Search API
client = Query(**oauth)
tweets = client.search_tweets(keywords='Bitcoin, #cryptocurrency', limit=10000)
tweet = next(tweets)
# Open data file
outfile = open("bitcoin_auto.csv", "a")
writer = csv.writer(outfile)
mydata = [
'DATE', 'TWEET', 'COMPOUND', 'NEGATIVE', 'NEUTRAL', 'POSITIVE', 'LATITUDE',
'LONGITUDE'
]
# writer.writerow(mydata)
def pre_process_text(tweet):
text = []
words_list = []
if __name__ == '__main__':
oauth = credsfromfile()
client = Query(**oauth)
with open(searchfile, 'rb') as f_search:
search_terms = [
term.strip() for term in f_search.readlines() if term.strip()
]
# Get tweets for specific search terms
for term in search_terms:
print "Collecting {term}".format(term=term)
search_data = []
tweets = client.search_tweets(
keywords="{term} -filter:retweets".format(term=term),
limit=float('inf'))
while True:
tweet = next(tweets, None)
if tweet is None:
break
elif tweet['id_str'] in tweet_ids:
continue
author_names.add(tweet['user']['screen_name'])
tweet_ids.add(tweet['id_str'])
search_data.append(tweet)
tweet['text'] = unicodedata.normalize(
'NFKD', strip_emoji(tweet['text'])).encode(
'ascii', 'ignore').encode('utf-8')
import json
import re
import csv
from nltk.twitter import Twitter
from nltk.twitter import Query, Streamer, Twitter, TweetViewer, TweetWriter, credsfromfile
from nltk.sentiment.vader import SentimentIntensityAnalyzer
tw = Twitter()
sid = SentimentIntensityAnalyzer()
# Grab credentials from file
oauth = credsfromfile()
# Search API
client = Query(**oauth)
tweets = client.search_tweets(keywords='Korean, summit', limit=10000)
tweet = next(tweets)
# Open data file
outfile = open("korean_summit_auto.csv", "a")
writer = csv.writer(outfile)
mydata = [
'DATE', 'TWEET', 'COMPOUND', 'NEGATIVE', 'NEUTRAL', 'POSITIVE', 'LATITUDE',
'LONGITUDE'
]
# writer.writerow(mydata)
def pre_process_text(tweet):
text = []
words_list = []
def search():
oauth = credsfromfile()
client = Query(**oauth)
df = pd.read_csv('twitter_users.csv')
df = df[df['Flag'] == 'Use']
terms = set([
'@' + u.replace('https://twitter.com/', '') for u in df['URL'].values
])
with open('terms.pkl', 'rb') as f:
terms = terms.union(pickle.load(f))
searches = 0
li_html = '<li>name={0} created={1} favorited={2} retweeted={3} \
{4} query={5}</li>'
for term in terms:
searches += 1
row = twitter_searches.find_one(query=term)
if row is not None:
if hours_from_now(row['search_date']) < 24:
continue
tweets = client.search_tweets(keywords=term + ' python http -RT',
lang='en',
limit=5)
for t in tweets:
if int(t['favorite_count']) == 0:
log.debug('No favorites')
continue
text = t['text']
dt = datetime.strptime(t['created_at'], '%a %b %d %H:%M:%S %z %Y')
if hours_from_now(dt) > 24:
continue
if core.not_english(text):
log.debug('Not english: {}'.format(text))
continue
log.debug('Searching for {}'.format(term))
uname = t['user']['screen_name']
uname_html = '<a href="https://twitter.com/{0}">{0}</a>'
users = [
v.replace('https://twitter.com/', '')
for v in pd.read_csv('twitter_users.csv')['URL'].values
]
with open('twitter_users.csv', 'a') as users_csv:
if uname not in set(users):
users_csv.write('{0},{1},Recommended\n'.format(
datetime.now(), 'https://twitter.com/' + uname))
html = li_html.format(uname_html.format(uname), t['created_at'],
t['favorite_count'], t['retweet_count'],
hrefs_from_text(text), term)
twitter_searches.upsert(
dict(query=term, search_date=datetime.now(), html=html),
['query', 'html'])
if searches == 150:
break
import re
import csv
from nltk.twitter import Twitter
from nltk.twitter import Query, Streamer, Twitter, TweetViewer, TweetWriter, credsfromfile
from nltk.sentiment.vader import SentimentIntensityAnalyzer
tw = Twitter()
sid = SentimentIntensityAnalyzer()
# Grab credentials from file
oauth = credsfromfile()
# Search API
client = Query(**oauth)
tweets = client.search_tweets(keywords='Syria', limit=10000)
tweet = next(tweets)
# Open data file
outfile = open("syria_auto.csv","a")
writer = csv.writer(outfile)
mydata = ['DATE', 'TWEET', 'COMPOUND', 'NEGATIVE', 'NEUTRAL', 'POSITIVE','LATITUDE','LONGITUDE']
# writer.writerow(mydata)
def pre_process_text(tweet):
text = []
words_list = []
clean_list = []
# Get all tweet text in english
if tweet['lang'] == "en":
import process_twt
from NBClassifier import NBClassifier
from SCClassifier import SCClassifier
from BGClassifier import BGClassifier
from nltk.corpus import twitter_samples, TwitterCorpusReader
import os
import pickle
import matplotlib.pyplot as plt
import numpy as np
# settings
oauth = credsfromfile()
client = Query(**oauth)
twtNum = 10
client.register(TweetViewer(limit=twtNum))
tweets_gen = client.search_tweets(keywords='hearthstone', lang='en')
tweets = []
slangdict = process_twt.get_slang_dict()
twt_list = []
for t in tweets_gen:
twt_list.append(process_twt.preprocess(t['text'], slangdict=slangdict))
twt_list = list(set(twt_list))
for t in twt_list[:twtNum]:
print t
fileIds = twitter_samples.fileids()
root = twitter_samples.root
# read tweet data from corpus
negReader = TwitterCorpusReader(root, fileIds[0])
# export TWITTER="twitter.txt"
from nltk.twitter import Twitter, Query, Streamer, credsfromfile
import pickle
from pprint import pprint
__author__ = 'kongaloosh'
import json
from pprint import pprint
with open('data/investments.json') as data_file:
# with open('data.json') as data_file:
oauth = credsfromfile()
data = json.load(data_file)
tw = Twitter()
client = Query(**oauth)
for i in range(len(data['investments'])):
if type(dict(data['investments'][i])):
tweets = client.search_tweets(keywords=data['investments'][i]['name'], limit=100)
tweets = list(tweets)
data['investments'][i]['tweets'] = tweets
with open('data_pickle.pkl', 'w') as outfile:
pickle.dump(data, outfile)
f = pickle.load(open('data_pickle.pkl', 'r'))
print(f)
luhn.display_comparison()
sumbasic.display_comparison()
res = input("Press 'r' to restart\n")
if res != 'r':
restart = False
elif choice == '2': # summarize a twitter topic
tweet_topic = input("Enter the topic you want a summary for\n")
# Authenticate and retrieve tweets based on user entered topic
oauth = credsfromfile()
client = Query(**oauth)
client.register(TweetWriter())
tweets = client.search_tweets(keywords=tweet_topic,
limit=100,
lang='en')
tweetSummarizer = TweetSummarizer()
# clean tweets and store in tweets.csv
rows = []
usable_rows = []
for tweet in tweets:
rows.append(str(tweet['text']))
if len(rows) > 0:
usable_rows = rows.copy()
for i in range(0, len(rows)):
rows[i] = clean_tweet(rows[i])
tweetSummarizer.store_full_tweets(rows[i])
with open('tweets.csv', 'w', encoding='utf-8') as csvfile:
def get_twiter(self, keywords):
client = Query(**oauth)
tweets = client.search_tweets(keywords, limit)
tweet = next(tweets)
return tweet
def search():
oauth = credsfromfile()
client = Query(**oauth)
df = pd.read_sql('SELECT URL FROM twitter_users',
db.executable.raw_connection())
users = set([u.replace('https://twitter.com/', '')
for u in df['URL'].values])
terms = set(['@' + u for u in users])
with open('terms.pkl', 'rb') as f:
terms = terms.union(pickle.load(f))
searches = 0
li_html = 'name={0} created={1} favorited={2} retweeted={3} \
{4} query={5}'
for term in terms:
searches += 1
row = twitter_searches.find_one(query=term)
if row is not None:
if hours_from_now(row['search_date']) < 24:
continue
tweets = client.search_tweets(keywords=term + ' python http -RT',
lang='en')
for t in tweets:
if int(t['favorite_count']) == 0:
log.debug('No favorites')
continue
text = t['text']
dt = datetime.strptime(t['created_at'],
'%a %b %d %H:%M:%S %z %Y')
if hours_from_now(dt) > 24:
continue
if core.not_english(text):
log.debug('Not english: {}'.format(text))
continue
log.debug('Searching for {}'.format(term))
uname = t['user']['screen_name']
uname_html = '<a href="https://twitter.com/{0}">{0}</a>'
if uname not in set(users):
db['twitter_users'].insert(
dict(Flag='Recommended', Date=datetime.now(),
URL='https://twitter.com/' + uname))
html = li_html.format(uname_html.format(uname), t['created_at'],
t['favorite_count'], t['retweet_count'],
hrefs_from_text(text), term)
twitter_searches.upsert(dict(query=term,
search_date=datetime.now(),
html=html),
['query', 'html'])
if searches == 150:
break
print('\n' + candidate['name'])
print("\n" + subject + " :")
print("Mots concernées : " + str(candidate['pro'] + candidate['cons']))
print("Avis pour : " + str(candidate['pro']))
print("Avis contre : " + str(candidate['cons']))
print("Sans avis : " + str(nb_tweets -
(candidate['pro'] + candidate['cons'])))
print("Indice pour : " + str(candidate['pro'] / nb_tweets))
print("Indice contre : " + str(candidate['cons'] / nb_tweets))
if (candidate['pro'] > candidate['cons']):
print("Les gens sont pour ce candidat")
elif (candidate['cons'] > candidate['pro']):
print("Les gens sont contre ce candidat")
else:
print("Les gens sont partagés")
print('\n\n')
if __name__ == '__main__':
print("Loadind tweets & analyzing ...")
subject = 'people_tweets'
for candidate in candidates_tweets:
tw = Twitter()
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(keywords=candidate['name'],
limit=nb_tweets)
analyze_tweets(candidate, tweets, subject)
print_results(candidate, subject)
import matplotlib.pyplot as plt
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import seaborn as sns
#Rest API
from nltk.twitter import Twitter
tw = Twitter()
# tw.tweets(keywords='LokSabhaElection2019', limit=2)
tw.tweets(keywords='LokSabhaElection2019', stream=False, limit=20)
## Read tweets
totaltweets = 0
oauth = credsfromfile()
client = Query(**oauth)
f = open('E:/temp/twitter.txt', 'w')
tweets = client.search_tweets(keywords='LokSabhaElection2019', limit=10000)
for tweet in tweets:
print(tweet['text'])
try:
f.write(tweet['text'])
totaltweets += 1
except Exception:
pass
f.close()
f = open('E:/temp/twitter.txt', 'a')
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(keywords='Elections2019', limit=10000)
for tweet in tweets:
print(tweet['text'])
return {i: data.count(i) for i in data}
#LIVE twitter feed
#------------------
#get 10 twitter messages with #whatdoyouwant
tw = Twitter()
tw.tweets(keywords='nationalgriduk', stream=False, limit=10)
brand = 'nationalgriduk'
#API keys
#------------------------
oauth = credsfromfile()
client = Query(**oauth)
tweets = client.search_tweets(keywords=brand, limit=20000)
tweet = next(tweets)
pprint(tweet, depth=1)
#make sure tweets can be encoded
non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)
#print(x.translate(non_bmp_map))
# Sentiment analysis
#-------------------------------
analyzer = SentimentIntensityAnalyzer() #vadersentiment object
Data = []
Words = []
Label = []
