def to_txt(write_path, read_path=None, verbose=False):
''' Function that takes in a path to the StanfordTweetData CSV
file, opens it, and writes the tweets with new lines to an output
file.
'''
read_path = self.load_data(verbose=verbose)
with open(read_path, 'r') as twitter_csv, open(write_path, 'w') as output:
reader = latin_csv_reader(twitter_csv, delimiter=',')
# For each line in CSV, write each tweet with a new line to the output
for line in reader:
output.write(line[5].encode('UTF-8') + '\n')
def to_txt(write_path, read_path=None, verbose=False):
''' Function that takes in a path to the StanfordTweetData CSV
file, opens it, and writes the tweets with new lines to an output
file.
'''
read_path = load_data(file_path=read_path, verbose=verbose)
with open(read_path, 'r') as twitter_csv, open(write_path, 'w') as output:
reader = latin_csv_reader(twitter_csv, delimiter=',')
# For each line in CSV, write each tweet with a new line to the output
for line in reader:
output.write(line[5].encode('UTF-8') + '\n')
def load_data(self, feat_extractor=None, verbose=False, return_iter=True):
''' Function that takes in a path to the StanfordTweetData CSV
file, opens it, and adds tweet strings and their respective
sentiments to a list
@Arguments:
file_path -- (optional) personal system file path to the
training.1600000.processed.noemoticon.csv data set (or others of
a similar structure)
The Stanford Sentiment140 Dataset is of the following format per row:
[polarity, tweet id, tweet date, query, user, tweet text]
feat_extractor -- (optional) a function that converts a tweet text string
and outputs a dictionary of features
verbose -- if True, this funciton shows logging data as it progresses
return_iter -- if True, return an iterator over tuples of (record, sentiment);
if False, return a list of such tuples
@Return:
A list of tuples of the following format:
(tweets/features, sentiment label)
'''
tweet_to_sentiment = list()
# Open file path
try:
twitter_csv = open(self.file_path, 'r')
except IOError as e:
logger.exception("File I/O error, will try downloading...")
logger.info("Downloading...")
self.download_data(self.file_path)
twitter_csv = open(self.file_path, 'r')
# Perform parsing of CSV file
reader = latin_csv_reader(twitter_csv, delimiter=',')
for i, tweet in enumerate(reader):
# Prints progress every 10000 words read
if verbose and i % 10000 == 0:
logging.info("PROGRESS: at tweet #%s", i)
# Gets tweets string from line in csv
tweet_string = tweet[5]
# ensure feature is in Sentiment dictionary
try:
sent = sentiment_binary[int(tweet[0])]
# If a feat_extractor function was provided, apply it to tweet
if feat_extractor:
features = feat_extractor(tweet_string)
tweet_to_sentiment.append((features, sent))
else:
tweet_to_sentiment.append((tweet_string, sent))
# tally number of samples
self.samples += 1
except KeyError:
logger.debug("Sentiment score of {} skipped.".format(tweet[0]))
twitter_csv.close()
# shuffle dataset
random.shuffle(tweet_to_sentiment)
# return list or iterator
if return_iter:
return iter(tweet_to_sentiment)
else:
return tweet_to_sentiment
def load_data(self,
feat_extractor=None,
verbose=False,
return_iter=True,
rng_seed=None):
''' Function that takes in a path to the StanfordTweetData CSV
file, opens it, and adds tweet strings and their respective
sentiments to a list
@Arguments:
file_path -- (optional) personal system file path to the
training.1600000.processed.noemoticon.csv data set (or others of
a similar structure)
The Stanford Sentiment140 Dataset is of the following format per row:
[polarity, tweet id, tweet date, query, user, tweet text]
feat_extractor -- (optional) a function that converts a tweet text string
and outputs a dictionary of features
verbose -- if True, this funciton shows logging data as it progresses
return_iter -- if True, return an iterator over tuples of (record, sentiment);
if False, return a list of such tuples
@Return:
A list of tuples of the following format:
(tweets/features, sentiment label)
'''
tweet_to_sentiment = list()
# Open file path
try:
twitter_csv = open(self.file_path, 'r')
except IOError as e:
logger.exception("File I/O error, will try downloading...")
logger.info("Downloading...")
self.download_data(self.file_path)
twitter_csv = open(self.file_path, 'r')
# Perform parsing of CSV file
reader = latin_csv_reader(twitter_csv, delimiter=',')
for i, tweet in enumerate(reader):
# Prints progress every 10000 words read
if verbose and i % 10000 == 0:
logging.info("PROGRESS: at tweet #%s", i)
# Gets tweets string from line in csv
tweet_string = tweet[5]
# ensure feature is in Sentiment dictionary
try:
sent = sentiment_binary[int(tweet[0])]
# If a feat_extractor function was provided, apply it to tweet
if feat_extractor:
features = feat_extractor(tweet_string)
tweet_to_sentiment.append((features, sent))
else:
tweet_to_sentiment.append((tweet_string, sent))
# tally number of samples
self.samples += 1
except KeyError:
logger.debug("Sentiment score of {} skipped.".format(tweet[0]))
twitter_csv.close()
# shuffle dataset
random.seed(rng_seed)
random.shuffle(tweet_to_sentiment)
# return list or iterator
if return_iter:
return iter(tweet_to_sentiment)
else:
return tweet_to_sentiment
# Open file path
if file_path:
try:
twitter_csv = open(file_path, 'r')
except IOError, e:
print "IO Error:", e.code, file_path
else:
# Dowloads and saves locally the zip file from internet
file_path = get_file("http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip")
with ZipFile(file_path, 'r') as zp:
twitter_csv = zp.open('training.1600000.processed.noemoticon.csv')
# Perform parsing of CSV file
reader = latin_csv_reader(twitter_csv, delimiter=',')
for i, tweet in enumerate(reader):
# Prints progress every 10000 words read
if verbose and i % 10000 == 0:
logging.info("PROGRESS: at tweet #%s", i)
# Gets tweets string from line in csv
tweet_string = tweet[5]
# Gets feature from Sentiment dictionary
sent = Sentiment[int(tweet[0])]
# If a feat_extractor function was provided, apply it to tweet
if feat_extractor:
features = feat_extractor(tweet_string)
tweet_to_sentiment.append((features, sent))
else:
tweet_to_sentiment.append((tweet_string, sent))
# Open file path
if file_path:
try:
twitter_csv = open(file_path, 'r')
except IOError, e:
print "IO Error:", e.code, file_path
else:
# Dowloads and saves locally the zip file from internet
file_path = get_file(
"http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip")
with ZipFile(file_path, 'r') as zp:
twitter_csv = zp.open('training.1600000.processed.noemoticon.csv')
# Perform parsing of CSV file
reader = latin_csv_reader(twitter_csv, delimiter=',')
for i, tweet in enumerate(reader):
# Prints progress every 10000 words read
if verbose and i % 10000 == 0:
logging.info("PROGRESS: at tweet #%s", i)
# Gets tweets string from line in csv
tweet_string = tweet[5]
# Gets feature from Sentiment dictionary
sent = Sentiment[int(tweet[0])]
# If a feat_extractor function was provided, apply it to tweet
if feat_extractor:
features = feat_extractor(tweet_string)
tweet_to_sentiment.append((features, sent))
else:
tweet_to_sentiment.append((tweet_string, sent))