def classify(processed_csv, test_file=True, **params):
positive_words = utils.file_to_wordset(params.pop('positive_words'))
negative_words = utils.file_to_wordset(params.pop('negative_words'))
predictions = []
with open(processed_csv, 'r', encoding='ISO-8859-1') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if test_file:
tweet_id = row['ItemID']
tweet = row['SentimentText']
else:
tweet_id = row['ItemID']
sentiment = row['Sentiment']
tweet = row['SentimentText']
pos_count, neg_count = 0, 0
for word in tweet.split():
if word in positive_words:
pos_count += 1
elif word in negative_words:
neg_count += 1
# print pos_count, neg_count
prediction = 1 if pos_count >= neg_count else 0
if test_file:
predictions.append((tweet_id, prediction))
else:
predictions.append((tweet_id, sentiment, prediction))
return predictions
def classify(processed_csv, test_file=True, **params):
positive_words = utils.file_to_wordset(params.pop('positive_words'))
negative_words = utils.file_to_wordset(params.pop('negative_words'))
predictions = []
with open(processed_csv, 'r') as csv:
for line in csv:
if test_file:
tweet_id, tweet = line.strip().split(',')
else:
tweet_id, label, tweet = line.strip().split(',')
pos_count, neg_count = 0, 0
for word in tweet.split():
if word in positive_words:
pos_count += 1
elif word in negative_words:
neg_count += 1
prediction = 1 if pos_count >= neg_count else 0
if test_file:
predictions.append((tweet_id, prediction))
else:
predictions.append((tweet_id, int(label), prediction))
return predictions
def classify(processed_csv, service_port, test_file=True, **params):
positive_words = file_to_wordset(params.pop('positive_words'), service_port)
negative_words = file_to_wordset(params.pop('negative_words'), service_port)
predictions = []
for row in parse_csv(processed_csv, service_port):
tweet_id = row.field[1]
tweet = row.field[2]
if not test_file:
label = row.field[0]
pos_count, neg_count = 0, 0
for word in tweet.split():
if word in positive_words:
pos_count += 1
elif word in negative_words:
neg_count += 1
# print pos_count, neg_count
prediction = 1 if pos_count >= neg_count else 0
if test_file:
predictions.append((tweet_id, prediction))
else:
predictions.append((tweet_id, int(label), prediction))
return predictions
