def generate_response(text):
# blob = TextBlob(message, classifier=CLASSIFICATION)
# text_type = blob.classify(message)
blob = TextBlob(text)
text_type = TEXT_STATEMENT
nouns_str = get_key_nouns_str(blob)
sentiment = blob.sentiment(text).polarity
response = dbutil.find_chat_response(text, text_type, nouns_str, sentiment)
return response
)
## tidy up the Tweets and send each to the AYLIEN Text API
for c, result in enumerate(results, start=1):
tweet = result.text
client = TextBlob(tweet)
#tidy_tweet = tweet.strip().encode('ascii', 'ignore')
if len(tweet) == 0:
print('Empty Tweet')
continue
#response = client.sentiment
#response = client.sentiment({'text': tidy_tweet})
response = client.sentiment({'text': tweet})
csv_writer.writerow({
'Tweet': response['text'],
'Sentiment': response['polarity']
})
print("Analyzed Tweet {}".format(c))
## count the data in the Sentiment column of the CSV file
with open(file_name, 'r') as data:
counter = Counter()
for row in csv.DictReader(data):
counter[row['Sentiment']] += 1
positive = counter['positive']
