def parse_all():
"""
This method parses all used datasets whether they aren't already parsed.
:return: None
"""
bn2wn = build_bn2wns()
# if Eurosense parsed sentences doesn't exists
if not os.path.isfile(EURO_SENTENCES):
write_file(EURO_SENTENCES, parser_eurosense(bn2wn))
# if SEW parsed sentences doesn't exists
if not os.path.isfile(SEW_SENTENCES):
write_file(SEW_SENTENCES, parser_sew(bn2wn))
# if TOM parsed sentences doesn't exists
if not os.path.isfile(TOM_SENTENCES):
wns2bn = build_wns2bn()
write_file(TOM_SENTENCES, parser_tom(wns2bn))
'''
def write_csv(data, filename):
with open(filename, 'wb') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(data)
if __name__ == '__main__':
# get path for all outputs
inp, urls, tags, replyats, sentiment, trends = txu.extract_arguments(n=7)
tweets = twu.get_input(inp)
tweets_text = "\n".join(tweets)
# extract all URLs in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.get_urls(tweets_text)), urls)
print "Time taken in extracting URLs: ", time.clock() - start
# extract all hashtags in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.extract_hashtags(tweets_text)), tags)
print "Time taken in extracting hashtags: ", time.clock() - start
# extract all replyats in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.extract_replyat(tweets_text)), replyats)
print "Time taken in extracting replyats: ", time.clock() - start
# get sentiments in the tweets and write in a file
start = time.clock()
write_csv(st.get_sentiments(tweets), sentiment)
import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the first feature which would produce the total count for each word in a file ft1.txt
'''
# Calculate the time when the processing starts
start = time.clock()
inp, outp = txu.extract_arguments()
# The mode of input can be a text file or twitter api json
tweets = '\n'.join(twu.get_input(inp))
words = txu.extract_words(tweets)
counter = txu.get_counter(words)
txu.write_file("\n".join("{} \t\t\t\t\t {}".format(k, v) for k, v in sorted(dict(counter).items())),outp)
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing word count: ", end - start
import time
import text_utils as txu
import twitter_utils as twu
'''
Implementing the first feature which would produce the total count for each word in a file ft1.txt
'''
# Calculate the time when the processing starts
start = time.clock()
inp, outp = txu.extract_arguments()
# The mode of input can be a text file or twitter api json
tweets = '\n'.join(twu.get_input(inp))
words = txu.extract_words(tweets)
counter = txu.get_counter(words)
txu.write_file(
"\n".join("{} \t\t\t\t\t {}".format(k, v)
for k, v in sorted(dict(counter).items())), outp)
# Calculate the time processing ends
end = time.clock()
# Print total time taken
print "Total time taken in processing word count: ", end - start
def write_csv(data, filename):
with open(filename, 'wb') as csvfile:
writer = csv.writer(csvfile)
writer.writerows(data)
if __name__ == '__main__':
# get path for all outputs
inp, urls, tags, replyats, sentiment, trends = txu.extract_arguments(n=7)
tweets = twu.get_input(inp)
tweets_text = "\n".join(tweets)
# extract all URLs in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.get_urls(tweets_text)), urls)
print "Time taken in extracting URLs: ", time.clock() - start
# extract all hashtags in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.extract_hashtags(tweets_text)), tags)
print "Time taken in extracting hashtags: ", time.clock() - start
# extract all replyats in the tweets and write in a file
start = time.clock()
txu.write_file('\n'.join(txu.extract_replyat(tweets_text)), replyats)
print "Time taken in extracting replyats: ", time.clock() - start
# get sentiments in the tweets and write in a file
start = time.clock()
write_csv(st.get_sentiments(tweets), sentiment)
