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']