wait = raw_input('press enter to continue')
print('')
print('')
print('')
print('############################################')
print('# Combined Example #')
print('############################################')
print('')
print('')
print('Processing text: ', demo_text)
print('')
response = alchemyapi.combined('text', demo_text)
if response['status'] == 'OK':
print('## Response Object ##')
print(json.dumps(response, indent=4))
print('')
print('## Keywords ##')
for keyword in response['keywords']:
print(keyword['text'], ' : ', keyword['relevance'])
print('')
print('## Concepts ##')
for concept in response['concepts']:
print(concept['text'], ' : ', concept['relevance'])
print('Checking imagetagging . . . ')
response = alchemyapi.imageTagging('text', test_text)
assert(response['status'] == 'ERROR')
response = alchemyapi.imageTagging('html', test_html)
assert(response['status'] == 'ERROR')
response = alchemyapi.imageTagging('url', test_url)
assert(response['status'] == 'OK')
response = alchemyapi.imageTagging('image', test_jpg)
assert(response['status'] == 'OK')
print('Image tagging tests complete!')
print('')
print('')
# combined
print('Checking combined . . . ')
response = alchemyapi.combined('text', test_text)
assert(response['status'] == 'OK')
response = alchemyapi.combined('html', test_html)
assert(response['status'] == 'ERROR')
response = alchemyapi.combined('url', test_url)
assert(response['status'] == 'OK')
print('Combined tests complete!')
print('')
print('')
# taxonomy
print('Checking taxonomy . . . ')
response = alchemyapi.taxonomy('text', test_text)
assert(response['status'] == 'OK')
response = alchemyapi.taxonomy('html', test_html, {'url': 'test'})
assert(response['status'] == 'OK')
print(category['label'], ' : ', category['score'])
print('')
else:
print('Error in taxonomy call: ', response['statusInfo'])
print('')
print('')
print('# Combined Example #')
response = alchemyapi.combined('text', demo_text)
if response['status'] == 'OK':
print('## Response Object ##')
with open('combied.js', 'w') as outfile:
json.dump(response,outfile, indent =4)
print('')
print('## Keywords ##')
for keyword in response['keywords']:
print(keyword['text'], ' : ', keyword['relevance'])
print('')
print('## Concepts ##')
for concept in response['concepts']:
print("")
print("")
print("")
print("")
print("############################################")
print("# Combined Example #")
print("############################################")
print("")
print("")
print("Processing text: ", demo_text)
print("")
response = alchemyapi.combined("text", demo_text)
if response["status"] == "OK":
print("## Response Object ##")
print(json.dumps(response, indent=4))
print("")
print("## Keywords ##")
for keyword in response["keywords"]:
print(keyword["text"], " : ", keyword["relevance"])
print("")
print("## Concepts ##")
for concept in response["concepts"]:
print(concept["text"], " : ", concept["relevance"])
class FileStore:
def __init__(self, filename, language):
self.al = AlchemyAPI('bf18ed72384724d86425c8674204039f87352870')
self.filename = filename
self.language = language
self.filterKeys = {'created_at': u'[\'created_at\']', 'id': u'[\'id\']', 'lang': u'[\'lang\']', 'tweet_urls': u'[\'entities\'][\'urls\']', 'tweet_hashtags': u'[\'entities\'][\'hashtags\']', 'user_location': u'[\'user\'][\'location\']', 'keywords': u'[\'alchemy\'][\'keywords\']', 'user_screen_name': u'[\'user\'][\'screen_name\']', 'text': u'[\'text\']', 'text_'+self.language : u'[\'text\']'}
def extract_entity(self, rawTweet, filteredTweet):
entities = ['City', 'Country', 'Person', 'Organization', 'PrintMedia']
try:
for i in rawTweet['alchemy']['entities']:
if i['type'] in entities:
if not filteredTweet.has_key(i['type']):
filteredTweet[i['type']] = []
filteredTweet[i['type']].append(i['text'])
except:
pass
def extract_concepts(self, rawTweet, filteredTweet):
try:
filteredTweet['concepts'] = []
for i in rawTweet['alchemy']['concepts']:
filteredTweet['concepts'].append(i['text'])
filteredTweet['concepts'].append(i['dbpedia'])
except:
pass
#rawTweet['alchemy']
def simplify(self, tweetObj, simplifyKeys):
for key,field in simplifyKeys:
if tweetObj.has_key(key):
tempObjList = tweetObj[key]
tweetObj[key] = [el[field] for el in tempObjList]
def dateConvert(self, tweetObj):
created_at = tweetObj['created_at']
tweetObj['created_at'] = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.strptime(created_at,'%a %b %d %H:%M:%S +0000 %Y'))
def translate(self, tweetObj):
lang = ['ar', 'de', 'en', 'fr', 'ru']
lang.remove(tweetObj['lang'])
tweet_lang = tweetObj['lang']
tweetObj['text_'+tweet_lang] = tweetObj['text']
for i in lang:
if not tweetObj.has_key('text_'+i):
text_to_translate = ' '.join(tweetObj['text_'+tweet_lang].split('\n'))
tweetObj['text_'+i] = translate(text_to_translate, tweet_lang, i)
elif tweetObj['text_'+i].find('TranslateApiException:') != -1:
text_to_translate = ' '.join(tweetObj['text_'+tweet_lang].split('\n'))
tweetObj['text_'+i] = translate(text_to_translate, tweet_lang, i)
def filter(self, tweet):
filteredTweet = {}
flattenedJson = {}
lang = ['ar', 'de', 'en', 'fr', 'ru']
for key in self.filterKeys:
try:
filteredTweet[key] = eval('tweet' + self.filterKeys[key])
except:
pass
self.extract_entity(tweet, filteredTweet)
self.extract_concepts(tweet, filteredTweet)
self.simplify(filteredTweet, [('tweet_urls', 'expanded_url'), ('tweet_hashtags', 'text'), ('keywords', 'text')])
try:
self.dateConvert(filteredTweet)
except:
pass
filteredTweet['date'] = filteredTweet['created_at'][:10]
if filteredTweet.has_key('text_'+filteredTweet['lang']):
filteredTweet['text'] = filteredTweet['text_'+filteredTweet['lang']]
for i in lang:
if filteredTweet.has_key('text_'+i):
filteredTweet['text_'+i] = filteredTweet['text_'+i].replace('\\', '')
return filteredTweet
def storeFilteredData(self, data):
with codecs.open(self.filename+'_tweets_filtered'+'.json', 'w', encoding="utf8") as fileHandle:
json.dump([self.filter(tweetData) for tweetData in data], fileHandle)
fileHandle.close()
def storeTweet(self, data):
with codecs.open(self.filename+'_tweets'+'.txt', 'w', encoding="utf8") as fileHandle:
for tweet in data:
fileHandle.write(str(tweet["id"])+'--->'+tweet['text']+'\n')
fileHandle.close()
def jsonLoad(self):
data = []
try:
with codecs.open(self.filename+'.json', 'r', encoding="ISO-8859-1") as fileHandle:
try:
data = json.load(fileHandle)
except:
pass
except:
pass
return data
def jsonStore(self, data):
storedJson = self.jsonLoad()
uniqueTweets = self.deduplicate(data + storedJson)
with codecs.open(self.filename+'.json', 'w', encoding="ISO-8859-1") as fileHandle:
json.dump(uniqueTweets, fileHandle)
fileHandle.close()
self.storeTweet(uniqueTweets)
self.storeFilteredData(uniqueTweets)
def store(self, data):
self.jsonStore(data)
def tag(self, tweet):
if not tweet.has_key('alchemy'):
if tweet['lang'] == 'de':
tweet_text = tweet['text_de']
else:
tweet_text = tweet['text_en']
tweet['alchemy'] = self.al.combined('text', tweet_text)
elif tweet['alchemy']['status'] == 'ERROR':
tweet_text = tweet['text']
'''
if tweet['lang'] == 'de':
tweet_text = tweet['text_de']
elif tweet['lang'] == 'en':
tweet_text = tweet['text_en']
'''
tweet['alchemy'] = self.al.combined('text', tweet_text)
#pprint.pprint( tweet['alchemy'])
def deduplicate(self, data):
uniqueTweets = {}
count = 0
try:
for tweet in data:
if not uniqueTweets.has_key(tweet['id']):
print "translation and tagging: "+str(count)
count = count+1
self.translate(tweet)
self.tag(tweet)
uniqueTweets[tweet['id']] = tweet
#print (tweet['text'])
print(len(uniqueTweets.values()))
except:
pass
return uniqueTweets.values()
def load(self):
data = []
try:
with codecs.open(self.filename+'.dat', 'rb', encoding="ISO-8859-1") as fileHandle:
while True:
try:
data.append(cPickle.load(fileHandle))
except:
break
except:
pass
return data
args.corpusloc += '/'
topn = 0 - args.nTOpropose
#stpwrds=loadStopwords("stopwords.french.list")
#print len(stpwrds)
start = time.time()
#vectorizer = CountVectorizer(ngram_range=(1,args.ngram),lowercase=True,stop_words=stpwrds)
[txtCorpus, tagsw] = readXMLCorpusFrom(args.corpusloc)
#print tagsw[11]
#print len(txtCorpus)
tgsugtop = []
alchemyapi = AlchemyAPI()
for docX in txtCorpus:
#print docX
tagsug = []
response = alchemyapi.combined('text', docX)
if response['status'] == 'OK':
for keyword in response['keywords']:
tagsug.append([keyword['text'], keyword['relevance']])
for entity in response['entities']:
tagsug.append([entity['text'], entity['relevance']])
for tgsg in sorted(tagsug, key=lambda x: x[1])[:10]:
tgsugtop.append(tgsg[0])
print tgsg[0]
#--------------------------------------
#f=open('../../results/tfidfsuggestion/'+args.resultfile+"_"+str(args.nTOpropose)+".res", 'wb')
f = open(
"../../../results/tooleval/alchemyAPItagger/" + args.resultfile + "_" +
str(args.nTOpropose) + ".res", 'wb')
class FileStore:
def __init__(self, filename, language):
self.al = AlchemyAPI('bf18ed72384724d86425c8674204039f87352870')
self.filename = filename
self.language = language
self.filterKeys = {
'created_at': u'[\'created_at\']',
'id': u'[\'id\']',
'lang': u'[\'lang\']',
'tweet_urls': u'[\'entities\'][\'urls\']',
'tweet_hashtags': u'[\'entities\'][\'hashtags\']',
'user_location': u'[\'user\'][\'location\']',
'keywords': u'[\'alchemy\'][\'keywords\']',
'user_screen_name': u'[\'user\'][\'screen_name\']',
'text': u'[\'text\']',
'text_' + self.language: u'[\'text\']'
}
def extract_entity(self, rawTweet, filteredTweet):
entities = ['City', 'Country', 'Person', 'Organization', 'PrintMedia']
try:
for i in rawTweet['alchemy']['entities']:
if i['type'] in entities:
if not filteredTweet.has_key(i['type']):
filteredTweet[i['type']] = []
filteredTweet[i['type']].append(i['text'])
except:
pass
def extract_concepts(self, rawTweet, filteredTweet):
try:
filteredTweet['concepts'] = []
for i in rawTweet['alchemy']['concepts']:
filteredTweet['concepts'].append(i['text'])
filteredTweet['concepts'].append(i['dbpedia'])
except:
pass
#rawTweet['alchemy']
def simplify(self, tweetObj, simplifyKeys):
for key, field in simplifyKeys:
if tweetObj.has_key(key):
tempObjList = tweetObj[key]
tweetObj[key] = [el[field] for el in tempObjList]
def dateConvert(self, tweetObj):
created_at = tweetObj['created_at']
tweetObj['created_at'] = time.strftime(
'%Y-%m-%dT%H:%M:%SZ',
time.strptime(created_at, '%a %b %d %H:%M:%S +0000 %Y'))
def translate(self, tweetObj):
lang = ['ar', 'de', 'en', 'fr', 'ru']
lang.remove(tweetObj['lang'])
tweet_lang = tweetObj['lang']
tweetObj['text_' + tweet_lang] = tweetObj['text']
for i in lang:
if not tweetObj.has_key('text_' + i):
text_to_translate = ' '.join(tweetObj['text_' +
tweet_lang].split('\n'))
tweetObj['text_' + i] = translate(text_to_translate,
tweet_lang, i)
elif tweetObj['text_' + i].find('TranslateApiException:') != -1:
text_to_translate = ' '.join(tweetObj['text_' +
tweet_lang].split('\n'))
tweetObj['text_' + i] = translate(text_to_translate,
tweet_lang, i)
def filter(self, tweet):
filteredTweet = {}
flattenedJson = {}
lang = ['ar', 'de', 'en', 'fr', 'ru']
for key in self.filterKeys:
try:
filteredTweet[key] = eval('tweet' + self.filterKeys[key])
except:
pass
self.extract_entity(tweet, filteredTweet)
self.extract_concepts(tweet, filteredTweet)
self.simplify(filteredTweet, [('tweet_urls', 'expanded_url'),
('tweet_hashtags', 'text'),
('keywords', 'text')])
try:
self.dateConvert(filteredTweet)
except:
pass
filteredTweet['date'] = filteredTweet['created_at'][:10]
if filteredTweet.has_key('text_' + filteredTweet['lang']):
filteredTweet['text'] = filteredTweet['text_' +
filteredTweet['lang']]
for i in lang:
if filteredTweet.has_key('text_' + i):
filteredTweet['text_' + i] = filteredTweet['text_' +
i].replace(
'\\', '')
return filteredTweet
def storeFilteredData(self, data):
with codecs.open(self.filename + '_tweets_filtered' + '.json',
'w',
encoding="utf8") as fileHandle:
json.dump([self.filter(tweetData) for tweetData in data],
fileHandle)
fileHandle.close()
def storeTweet(self, data):
with codecs.open(self.filename + '_tweets' + '.txt',
'w',
encoding="utf8") as fileHandle:
for tweet in data:
fileHandle.write(
str(tweet["id"]) + '--->' + tweet['text'] + '\n')
fileHandle.close()
def jsonLoad(self):
data = []
try:
with codecs.open(self.filename + '.json',
'r',
encoding="ISO-8859-1") as fileHandle:
try:
data = json.load(fileHandle)
except:
pass
except:
pass
return data
def jsonStore(self, data):
storedJson = self.jsonLoad()
uniqueTweets = self.deduplicate(data + storedJson)
with codecs.open(self.filename + '.json', 'w',
encoding="ISO-8859-1") as fileHandle:
json.dump(uniqueTweets, fileHandle)
fileHandle.close()
self.storeTweet(uniqueTweets)
self.storeFilteredData(uniqueTweets)
def store(self, data):
self.jsonStore(data)
def tag(self, tweet):
if not tweet.has_key('alchemy'):
if tweet['lang'] == 'de':
tweet_text = tweet['text_de']
else:
tweet_text = tweet['text_en']
tweet['alchemy'] = self.al.combined('text', tweet_text)
elif tweet['alchemy']['status'] == 'ERROR':
tweet_text = tweet['text']
'''
if tweet['lang'] == 'de':
tweet_text = tweet['text_de']
elif tweet['lang'] == 'en':
tweet_text = tweet['text_en']
'''
tweet['alchemy'] = self.al.combined('text', tweet_text)
#pprint.pprint( tweet['alchemy'])
def deduplicate(self, data):
uniqueTweets = {}
count = 0
try:
for tweet in data:
if not uniqueTweets.has_key(tweet['id']):
print "translation and tagging: " + str(count)
count = count + 1
self.translate(tweet)
self.tag(tweet)
uniqueTweets[tweet['id']] = tweet
#print (tweet['text'])
print(len(uniqueTweets.values()))
except:
pass
return uniqueTweets.values()
def load(self):
data = []
try:
with codecs.open(self.filename + '.dat',
'rb',
encoding="ISO-8859-1") as fileHandle:
while True:
try:
data.append(cPickle.load(fileHandle))
except:
break
except:
pass
return data
print('Checking imagetagging . . . ')
response = alchemyapi.imageTagging('text', test_text)
assert (response['status'] == 'ERROR')
response = alchemyapi.imageTagging('html', test_html)
assert (response['status'] == 'ERROR')
response = alchemyapi.imageTagging('url', test_url)
assert (response['status'] == 'OK')
response = alchemyapi.imageTagging('image', test_jpg)
assert (response['status'] == 'OK')
print('Image tagging tests complete!')
print('')
print('')
# combined
print('Checking combined . . . ')
response = alchemyapi.combined('text', test_text)
assert (response['status'] == 'OK')
response = alchemyapi.combined('html', test_html)
assert (response['status'] == 'ERROR')
response = alchemyapi.combined('url', test_url)
assert (response['status'] == 'OK')
print('Combined tests complete!')
print('')
print('')
# taxonomy
print('Checking taxonomy . . . ')
response = alchemyapi.taxonomy('text', test_text)
assert (response['status'] == 'OK')
response = alchemyapi.taxonomy('html', test_html, {'url': 'test'})
assert (response['status'] == 'OK')
def user_analysis_sentiments(request):
if request.method == 'GET':
print request.GET.get('user', '')
user = request.GET.get('user', '')
messages = []
message = Message.objects.filter(user_send=user.decode("utf8"))
for m in message:
messages.append(m.message_text)
text = ",".join(messages)
alchemyapi = AlchemyAPI()
#keywords
response = alchemyapi.keywords('text', text, {'sentiment': 1})
if response['status'] == 'OK':
keywords = []
for keyword in response['keywords']:
keyword_text = keyword['text'].encode('utf-8')
keyword_relevance = keyword['relevance']
keyword_sentiment = keyword['sentiment']['type']
key_word = {'keyword_text': keyword_text, 'keyword_relevance': keyword_relevance,
'keyword_sentiment': keyword_sentiment}
keywords.append(key_word)
else:
print('Error in keyword extaction call: ', response['statusInfo'])
response = alchemyapi.concepts('text', text)
if response['status'] == 'OK':
concepts = []
for concept in response['concepts']:
concept_text = concept['text']
concept_relevance = concept['relevance']
concept_entity = {'concept_text': concept_text, 'concept_relevance': concept_relevance}
concepts.append(concept_entity)
else:
print('Error in concept tagging call: ', response['statusInfo'])
response = alchemyapi.language('text', text)
if response['status'] == 'OK':
print(response['wikipedia'])
language = response['language']
iso_639_1 = response['iso-639-1']
native_speakers = response['native-speakers']
wikipedia = response['wikipedia']
language_id = {'language': language, 'iso_639_1': iso_639_1, 'native_speakers': native_speakers, 'wikipedia': wikipedia}
else:
print('Error in language detection call: ', response['statusInfo'])
response = alchemyapi.relations('text', text)
if response['status'] == 'OK':
relations = []
for relation in response['relations']:
if 'subject' in relation:
relation_subject_text = relation['subject']['text'].encode('utf-8')
if 'action' in relation:
relation_action_text = relation['action']['text'].encode('utf-8')
if 'object' in relation:
relation_object_text = relation['object']['text'].encode('utf-8')
relation_entity = {'relation_subject_text': relation_subject_text,
'relation_action_text': relation_action_text,
'relation_object_text': relation_object_text}
relations.append(relation_entity)
else:
print('Error in relation extaction call: ', response['statusInfo'])
response = alchemyapi.category('text', text)
if response['status'] == 'OK':
print('text: ', response['category'])
category = response['category']
print('score: ', response['score'])
score = response['score']
categories = {'category': category, 'score': score}
else:
print('Error in text categorization call: ', response['statusInfo'])
response = alchemyapi.taxonomy('text', text)
if response['status'] == 'OK':
taxonomies = []
for category in response['taxonomy']:
taxonomy_label = category['label']
taxonomy_score = category['score']
taxonomy = {'taxonomy_label': taxonomy_label, 'taxonomy_score': taxonomy_score}
taxonomies.append(taxonomy)
else:
print('Error in taxonomy call: ', response['statusInfo'])
response = alchemyapi.combined('text', text)
if response['status'] == 'OK':
print('## Response Object ##')
print(json.dumps(response, indent=4))
print('')
user = {'user_name': 'LOL', 'keywords': keywords, 'concepts': concepts, 'language_id': language_id,
'relations': relations, 'categories': categories, 'taxonomies': taxonomies}
return HttpResponse(json.dumps(user), content_type="application/json")
#d = []
#wiki_terms = pd.DataFrame(data=None,columns=['Title','URL','Entities','Keywords','Taxonomy','Concepts','Language','Author'])
#for i in range(len(wiki_db)):
# d = [wiki_db[i]['title'],wiki_db[i]['url'],wiki_db[i]['entities'],wiki_db[i]['keywords'],wiki_db[i]['taxonomy'],wiki_db[i]['concepts'],wiki_db[i]['language'],wiki_db[i]['author']]
# wiki_terms.loc[i]=d
#path = '/Users/michael/PycharmProjects/Startup Weekend/'
#wiki_terms.to_pickle('wiki_terms.pk1')
#wiki_terms.to_csv('wiki_terms2.csv')
wiki_terms = pd.read_pickle('wiki_terms.pk1')
#wiki_terms2 = pd.read_csv('wiki_terms2.csv')
news = alchemyapi.combined(
'url',
news_urls,
options={
'extract':
'page-image, entity, keyword, title, author, taxonomy, concept'
})
news_keywords = []
for i in range(len(news['keywords'])):
news_keywords.append(news['keywords'][i]['text'])
for i in range(len(news['entities'])):
news_keywords.append(news['entities'][i]['text'])
for i in range(len(news['concepts'])):
news_keywords.append(news['concepts'][i]['text'])
news_list = Counter(news_keywords)
wiki_list1 = []
data_dir = os.path.join(upper_dir, 'data')
filename = os.path.join(data_dir, 'Allmovie_2.csv.csv')
alchemy_language = AlchemyLanguageV1(
api_key='7a454d32972b85fd2599536ba9063e4d1530014b')
alchemyapi = AlchemyAPI()
f = open(filename, 'rb')
reader = csv.DictReader(f)
result = open('result_of_alchemyapi_overview_to_keywords.csv', 'w')
fieldnames = ['movie overview', 'movie keywords']
writer = csv.DictWriter(result, fieldnames=fieldnames)
writer.writeheader()
counter = 0
for line in reader:
counter = counter + 1
if counter >= 2501:
response = alchemyapi.combined('text', line['overview'])
if response['status'] == 'OK':
keywords = []
for keyword in response['keywords']:
if float(keyword['relevance']) > 0.5:
keywords.append(keyword['text'].encode('utf-8'))
print(keyword['text'], ' : ', keyword['relevance'])
writer.writerow({
'movie overview': line['overview'],
'movie keywords': keywords
})
f.close()
data.close()
def __init__(self):
##### Input file, can either be modified manually or with another program.
fo = open("answer.txt", "r")
text = open("answer.txt").read().splitlines()
index = len(text) - 1
##### Because I was working with a file that was updated very frequently,
##### I grab the most recent expression in the file.
text = text[index]
fo.close()
#### API instance creation
alchemyapi = AlchemyAPI()
print(text)
print('')
isCity = False
isWeather = False
wasIn = False
#### If there is no city name in our input, program asks for a city name until you specify one.
#### NOTICE: enter the city name between quotation marks. e.g. "Paris"
while (not isCity) or (not isWeather):
##### This is not the best solution ever, will update when I come up with a new one :P
if wasIn:
test = input("WHERE?")
print(test)
text = text + " " + test
# JSON result which has the intents we're looking for.
response = alchemyapi.combined('text', text)
# Uncomment the line below for the JSON result.
#print(json.dumps(response, indent=4))
##### If the sentence is not related with weather, an exception is thrown
if response['taxonomy']:
for x in response['taxonomy']:
if "weather" in x['label']:
isWeather = True
break
# A temporary solution for simple cases.
if "FORECAST" in text:
isWeather = True
elif "SUNNY" in text:
isWeather = True
elif "RAINY" in text:
isWeather = True
##### Distinguishing between cities and countries in order to prevent troubles
if response['entities']:
for x in response['entities']:
if "City" in x['type']:
isCity = True
break
wasIn = True
##### Variables below are passed to weather.py for further usage.
try:
if isCity:
self.city = response['entities'][0]['text'] #city name
except:
self.city = ""
try:
self.total_days = response['entities'][1]['text']
except:
self.total_days = "TODAY"
pass
