def do_analysis(names, texts):
alchemyapi = AlchemyAPI()
base=os.path.basename('transcripts/Democrats/dem-2-4-2016.txt')
debate_name = os.path.splitext(base)[0]
file_name = 'data/targeted sentiments/Democrat/' + str(debate_name) + '_sentiment.txt'
f = open(file_name, 'w')
filepath = "data/topics/Democratic Debates_Top Three Topics.csv"
all_topics = csv.DictReader(open(filepath, "r"))
dt_1 = []
for row in all_topics:
dt_1.append(row[debate_name])
dt_2 = [x.split('\'') for x in dt_1]
dt_3 = [y for x in dt_2[0] for y in x.split(',')]
for i in range(len(names)):
for j in range(3):
topic = [y for x in dt_2[j] for y in x.split(',')]
response = alchemyapi.sentiment_targeted('text', texts[i], topic[:(len(topic)-1)])
if response['status'] == 'OK':
f.write(topic[len(topic)-1] + ':\n')
f.write(names[i] + ' Sentiment \n')
f.write('type: ' + str(response['docSentiment']['type']) + '\n')
if 'score' in response['docSentiment']:
f.write('score: ' + str(response['docSentiment']['score']) + '\n \n')
else:
print('Error in sentiment analysis call: ',
response['statusInfo'])
def populateTweets(self):
self.lat, self.long, self.radius = self.findCoordinatesRadius()
geo = str(self.lat) + "," + str(self.long) + "," + str(self.radius) + "km"
tweets = api.search(q=self.search, lang='en', geocode=geo, rpp=100)
showcase = tweets[0:5]
self.showcase = []
for tweet in showcase:
self.showcase.append([tweet.text, tweet.user.screen_name])
hashtagsRaw = [tweet.entities['hashtags'] for tweet in tweets]
hashtagsList = list(itertools.chain.from_iterable(hashtagsRaw))
hashtags = [hash['text'] for hash in hashtagsList]
frequency = {}
for hashtag in hashtags:
frequency[hashtag] = hashtags.count(hashtag)
self.popularHashtags = dict(Counter(hashtags).most_common(5)).keys()
texts = [tweet.text for tweet in tweets]
self.sentiment = 0.0
alchemyapi = AlchemyAPI()
for text in texts:
response = alchemyapi.sentiment_targeted('text', text.lower(), self.search.lower())
if response['status'] != 'ERROR' and response['docSentiment']['type'] != 'neutral':
numeric = float(response['docSentiment']['score'])
self.sentiment = self.sentiment + (numeric / len(texts)) #computes average sentiment
def populateTweets(self):
self.lat, self.long, self.radius = self.findCoordinatesRadius()
geo = str(self.lat) + "," + str(self.long) + "," + str(
self.radius) + "km"
tweets = api.search(q=self.search, lang='en', geocode=geo, rpp=100)
showcase = tweets[0:5]
self.showcase = []
for tweet in showcase:
self.showcase.append([tweet.text, tweet.user.screen_name])
hashtagsRaw = [tweet.entities['hashtags'] for tweet in tweets]
hashtagsList = list(itertools.chain.from_iterable(hashtagsRaw))
hashtags = [hash['text'] for hash in hashtagsList]
frequency = {}
for hashtag in hashtags:
frequency[hashtag] = hashtags.count(hashtag)
self.popularHashtags = dict(Counter(hashtags).most_common(5)).keys()
texts = [tweet.text for tweet in tweets]
self.sentiment = 0.0
alchemyapi = AlchemyAPI()
for text in texts:
response = alchemyapi.sentiment_targeted('text', text.lower(),
self.search.lower())
if response['status'] != 'ERROR' and response['docSentiment'][
'type'] != 'neutral':
numeric = float(response['docSentiment']['score'])
self.sentiment = self.sentiment + (
numeric / len(texts)) #computes average sentiment
def writeResult(arr, outFile, search):
alchemyapi = AlchemyAPI(apiKey)
#alchemyapi = AlchemyAPI("89e395ea07490a40a55ccf241612724f80827956")
#alchemyapi = AlchemyAPI("f7e81de9b04fcb1eadc9469800a86a15bffd8ec3")
#alchemyapi = AlchemyAPI("d3547d0e12ac5425b57cf1d2e05280525224a109")
#alchemyapi = AlchemyAPI("6d03602e012eca8b7ab3ac92e37327950b1caa78")
#print "In write ",len(arr)
fi = open(outFile,'wb')
SEARCHTERM = search.lower()
writer = csv.writer(fi)
neg, pos, neu = 0,0,0
results = []
print "Starting AlchemyAPI"
for item in zip(*arr)[0]:
#print item
response = alchemyapi.sentiment_targeted('text', item, SEARCHTERM)
#print "maine response",response
try:
respType = response['docSentiment']['type']
#print "Response ",respType
except Exception, e:
#print e
continue
if respType == 'neutral': neu += 1
elif respType == 'positive': pos += 1
elif respType == 'negative': neg += 1
lst = [item,respType]
writer.writerow(lst)
results.append(lst)
def analyzeTweets(apiKey, arr, search, resultsArray):
alchemyapi = AlchemyAPI(apiKey)
SEARCHTERM = search.lower()
neg, pos, neu = 0,0,0
for item in arr:
#print item
response = alchemyapi.sentiment_targeted('text', item[0], SEARCHTERM)
print "maine response",response
try:
respType = response['docSentiment']['type']
#print "Response ",respType
except Exception, e:
#print e
continue
if respType == 'neutral': neu += 1
elif respType == 'positive': pos += 1
elif respType == 'negative': neg += 1
lst = [item[0],respType, item[1],item[2]]
resultsArray.append(lst)
is secure, we do not get access to the private https: URLs
img_url = media.images['standard_resolution'].url
img_response = alchemyapi.imageExtraction('url', img_url)
if img_response['status'] == 'OK':
print(json.dumps(img_response, indent=4))
else:
print('Error in image extraction call: ', img_response['statusInfo'])
'''
# Printing the caption of the image and doing sentiment analysis of the
# caption targeted towards CapitalOne
name = api.user(media.user.id)
print name
if hasattr(media, 'caption'):
print YELLOW+"Caption :"+ENDCOLOR, media.caption.text
response = alchemyapi.sentiment_targeted('text', media.caption.text,
'capital')
if response['status'] == 'OK':
print GREEN+'Sentiment type: '+ENDCOLOR, response['docSentiment']['type']
if 'score' in response['docSentiment']:
print GREEN + 'Sensitivity Score: '+ENDCOLOR, response['docSentiment']['score']
else:
print('Error in targeted sentiment analysis call: ',
response['statusInfo'])
# Printing Media and User Statistics: Media Likes, User Details include
# Number of Followers
# Number of Users Following
print('')
print('')
print('')
print('############################################')
print('# Targeted Sentiment Analysis Example #')
print('############################################')
print('')
print('')
print('Processing text: ', demo_text)
print('')
response = alchemyapi.sentiment_targeted('text',demo_text, 'Denver')
if response['status'] == 'OK':
print('## Response Object ##')
print(json.dumps(response, indent=4))
print('')
print('## Targeted Sentiment ##')
print('type: ', response['docSentiment']['type'])
if 'score' in response['docSentiment']:
print('score: ', response['docSentiment']['score'])
else:
print('Error in targeted sentiment analysis call: ', response['statusInfo'])
response = alchemyapi.sentiment('text', test_text);
assert(response['status'] == 'OK')
response = alchemyapi.sentiment('html', test_html);
assert(response['status'] == 'OK')
response = alchemyapi.sentiment('url', test_url);
assert(response['status'] == 'OK')
response = alchemyapi.sentiment('random', test_url);
assert(response['status'] == 'ERROR') #invalid flavor
print('Sentiment tests complete!')
print('')
#Targeted Sentiment
print('Checking targeted sentiment . . . ')
response = alchemyapi.sentiment_targeted('text', test_text, 'heart');
assert(response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('html', test_html, 'language');
assert(response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('url', test_url, 'Congress');
assert(response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('random', test_url, 'Congress');
assert(response['status'] == 'ERROR') #invalid flavor
response = alchemyapi.sentiment_targeted('text', test_text, None);
assert(response['status'] == 'ERROR') #missing target
print('Targeted sentiment tests complete!')
print('')
#Text
wait = raw_input('press enter to continue')
print('')
print('')
print('')
print('############################################')
print('# Targeted Sentiment Analysis Example #')
print('############################################')
print('')
print('')
print('Processing text: ', demo_text)
print('')
response = alchemyapi.sentiment_targeted('text', demo_text, 'Denver')
if response['status'] == 'OK':
print('## Response Object ##')
print(json.dumps(response, indent=4))
print('')
print('## Targeted Sentiment ##')
print('type: ', response['docSentiment']['type'])
if 'score' in response['docSentiment']:
print('score: ', response['docSentiment']['score'])
else:
print('Error in targeted sentiment analysis call: ',
response['statusInfo'])
#Sentiment
print('Checking sentiment . . . ')
response = alchemyapi.sentiment('text', test_text)
assert (response['status'] == 'OK')
response = alchemyapi.sentiment('html', test_html)
assert (response['status'] == 'OK')
response = alchemyapi.sentiment('url', test_url)
assert (response['status'] == 'OK')
response = alchemyapi.sentiment('random', test_url)
assert (response['status'] == 'ERROR') #invalid flavor
print('Sentiment tests complete!')
print('')
#Targeted Sentiment
print('Checking targeted sentiment . . . ')
response = alchemyapi.sentiment_targeted('text', test_text, 'heart')
assert (response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('html', test_html, 'language')
assert (response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('url', test_url, 'Congress')
assert (response['status'] == 'OK')
response = alchemyapi.sentiment_targeted('random', test_url, 'Congress')
assert (response['status'] == 'ERROR') #invalid flavor
response = alchemyapi.sentiment_targeted('text', test_text, None)
assert (response['status'] == 'ERROR') #missing target
print('Targeted sentiment tests complete!')
print('')
#Text
print('Checking text . . . ')
response = alchemyapi.text('text', test_text)
temp = open('review2dish.json', 'r')
analysis_temp = open('review2analysis.json', 'w')
reviews = json.load(temp)
results = {}
count = 0
request_count = 0
for item in reviews:
#print reviews[item][0]['review']
restaurant = reviews[item][0]['restaurant']
time = reviews[item][0]['time']
author = reviews[item][0]['author']
for t in reviews[item][0]['keyword']:
#pass
response = alchemyapi.sentiment_targeted('text',
reviews[item][0]['review'],
t.encode('utf8'))
request_count += 1
if response['status'] == 'OK' and response['docSentiment'].has_key(
'score'):
ana_result = {
'keyword': t.encode('utf8'),
'review': reviews[item][0]['review'],
'result': response['docSentiment'],
'restaurant': restaurant,
'time': time,
'author': author
}
if results.has_key(item):
results[item].append(ana_result)
print('Error in sentiment analysis call: ', response['statusInfo'])
print('')
print('')
print('')
print('############################################')
print('# Targeted Sentiment Analysis Example #')
print('############################################')
print('')
print('')
#print('Processing text: ', demo_text)
print('')
targeting = 'sky'
response = alchemyapi.sentiment_targeted('text', demo_text, targeting)
if response['status'] == 'OK':
print('## Response Object ##')
#print(json.dumps(response, indent=4))
print('')
print('## Targeted Sentiment ## of', targeting)
print('type: ', response['docSentiment']['type'])
if 'score' in response['docSentiment']:
print('score: ', response['docSentiment']['score'])
else:
print('Error in targeted sentiment analysis call: ',
response['statusInfo'])
try:
sock = urllib2.urlopen(response['items'][i]['link'], timeout=1)
# Avoid to put into variable if size HTML code is too big
if sys.getsizeof(sock.read()) < 4098871:
sock.close()
sock = urllib2.urlopen(response['items'][i]['link'])
content_HTML = sock.read()
sock.close()
except Exception:
print j + i + 1, 'urllib error'
# Targeted sentiment analysis
sentiment = None
score = None
mixed = None
alchemy_response = alchemyapi.sentiment_targeted(
'url', response['items'][i]['link'], topic)
if alchemy_response['status'] == 'OK':
sentiment = alchemy_response['docSentiment']['type']
if 'score' in alchemy_response['docSentiment']:
score = alchemy_response['docSentiment']['score']
if 'mixed' in alchemy_response['docSentiment']:
mixed = alchemy_response['docSentiment']['mixed']
else:
print j + i + 1, 'Error in targeted sentiment analysis call: ', alchemy_response[
'statusInfo']
# Keyword sentiment analysis
keyword_response = alchemyapi.keywords('url',
response['items'][i]['link'],
{'sentiment': 1})
key_sentiment = None
