class TweetAnalyser():
def __init__(self):
self.twitter_client = TwitterClient()
self.api = self.twitter_client.getTwitterClientAPI()
self.accounts = self.read_file()
self.results = []
self.accounts_counter = 0
def read_file(self):
with open(ACCOUNTS_FILE, "r") as f:
print("Readind file : [ {0} ]".format(ACCOUNTS_FILE))
content = f.read()
print("File : [ {0} ] is read".format(ACCOUNTS_FILE))
return content.splitlines()
def write_in_file(self, input_text_list):
with open(RESULTS_FILE, "w") as f:
f.write("\n".join(input_text_list))
def is_relevant(self, text):
text = text.lower()
return any(
key_word
for key_word in KEY_WORDS
if key_word in text
)
def get_position(self, text):
# Avoid sentiment returning 0
# return 0
text = text.lower()
if TOPICO == "politica":
text = re.sub(r'^https?:\/\/.*[\r\n]*', '', text, flags=re.MULTILINE)
clf = SentimentClassifier()
value = clf.predict(text)
else:
analysis = TextBlob(text)
value = analysis.sentiment.polarity
if value > .55:
return 1
elif value < .35 :
return -1
else:
return 0
def final_position(self, positions):
if len(positions) == 0:
return "There are not enough data to analyze the account"
account_position = sum(positions)/len(positions)
if account_position > .15 :
return "Follower"
elif account_position < -.15:
return "Opposition"
else:
return "Neutral"
def tweets_to_data_frame(self, tweets):
text = []
id = []
tweet_len = []
likes = []
rt = []
position = []
for tweet in tweets:
if not tweet.retweeted and self.is_relevant(tweet.text):
text.append(re.sub(r'^https?:\/\/.*[\r\n]*', '', tweet.text, flags=re.MULTILINE))
id.append(tweet.id)
tweet_len.append(len(tweet.text))
likes.append(tweet.favorite_count)
rt.append(tweet.retweet_count)
position.append(self.get_position(tweet.text))
data = {
"id": id,
"text": text,
"len tweet": tweet_len,
"likes": likes,
"rt": rt,
"position": position
}
return pandas.DataFrame(data)
def worker(self):
for account in self.accounts:
print("Computing account = {0}".format(account))
if account == "":
break
# Print progress of computing
self.accounts_counter += 1
procces_completed = str(
int(
(self.accounts_counter/len(self.accounts))*100
)
)
print(
"Process completed [ {0}% ]"\
.format(procces_completed)
)
try:
tweets = self.api.user_timeline(
screen_name=account,
count = 1000
)
df = self.tweets_to_data_frame(tweets)
account_result_file = "{0}{1}.csv".format(RESULTS_DIR, account)
df.to_csv(
account_result_file,
encoding='utf-8',
index=False
)
self.results.append(
"{0},{1}".format(account, self.final_position(df["position"]))
)
except TweepError as err:
print("The account [ {0} ] is private, skipping...".format(account))
def main(self):
print("Mining accounts ...")
self.worker()
print("Tweets saved in directory")
print("Saving results in [ {0} ]".format(RESULTS_FILE))
self.write_in_file(self.results)
print("Process finished")
