def get_keywords(text):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username="******",
password="******",
version="2017-02-27")
response = natural_language_understanding.analyze(
text=text,
features=[
Features.Entities(emotion=True, sentiment=True, limit=10),
Features.Keywords(emotion=True, sentiment=True, limit=10)
])
# print(json.dumps(response, indent=2))
to_ret = {}
for a in response["keywords"]:
to_ret.update({a['text']: '#'})
for a in response["entities"]:
try:
to_ret.update({a['text']: a['disambiguation']['dbpedia_resource']})
except:
to_ret.update({a['text']: '#'})
return to_ret
def eval_default():
response = nlu.analyze(
text='Bruce Banner is the Hulk and Bruce Wayne is BATMAN! '
'Superman fears not Banner, but Wayne.',
features=[features.Entities(),
features.Keywords()])
return jsonify(response)
def getSentimentAnalysis(url): """ Get sentiment analysis of a given URL :param: url: URL to get sentiment analysis :returns: Sentiment magnitude and emotion analysis in JSON format """ html = urllib.request.urlopen(url).read() soup = BeautifulSoup(html, 'html.parser') text = textFromHtml(html) natural_language_understanding = nl.enableWatsonNatLang() response = natural_language_understanding.analyze( text= text, features=[ # Get general sentiment of text Features.Sentiment( document=True ), # Get emotion towards relevant entities (max:3) Features.Entities( emotion=True, limit = 3 ) ] ) return json.dumps(response, indent=2)
def main():
credentials = eval("\n".join(open(CREDENTIALS, "r").readlines()))
natural_language_understanding = NaturalLanguageUnderstandingV1(
version="2017-02-27",
username=credentials["username"],
password=credentials["password"]
)
listOfFiles = [f for f in listdir(PATH) if isfile(join(PATH, f))]#["testData/354962.json"]
for fileName in listOfFiles:
fileIn = open(join(PATH, fileName), "r")
data = json.load(fileIn)
val = data["html_lawbox"]
if any(word in val for word in ["convict","acquit","guilty","innocen", "sentenc"]):
print("Good file: ", fileName)
for word in ["convict","acquit","guilty","innocen", "sentenc"]:
if word in val:
print "it has ", word
response = natural_language_understanding.analyze(
text=data["html_lawbox"],
features = [features.Entities(), features.Relations()]
)
print json.dumps(response, indent=2)
else:
print("Bad file: ", fileName)
def watsonanalyze(stringinput):
response = natural_language_understanding.analyze(
text=stringinput,
features=[
Features.Entities(emotion=True, sentiment=True, limit=2),
Features.Keywords(emotion=True, sentiment=True, limit=20)
])
return json.loads(json.dumps(response, indent=2))
def nlp(input_stuff):
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username='******',
password='******')
response = natural_language_understanding.analyze(
text=input_stuff, features=[features.Entities(),
features.Keywords()])
return (response["entities"])
def nl_processing(reqd_text):
response = natural_language_understanding.analyze(text=reqd_text,
features=[
features.Entities(),
features.Keywords(),
features.Emotion(),
features.Concepts(),
features.Sentiment()
])
return response
def nlp(input_stuff):
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username="******",
password="******")
response = natural_language_understanding.analyze(
text=input_stuff, features=[features.Entities(),
features.Keywords()])
return (response["entities"])
def call_nlgu(text_stmt):
Config=configparser.ConfigParser()
Config.read("watson.ini")
userid=ConfigSectionMap(Config,"Natural Language Understanding-RAI")['username']
pwd=ConfigSectionMap(Config,"Natural Language Understanding-RAI")['password']
natural_language_understanding = NaturalLanguageUnderstandingV1(version='2017-02-27',username=userid,password=pwd)
response = natural_language_understanding.analyze(text=text_stmt,features=[features.Entities(), features.Keywords()])
try:
print(json.dumps(response, indent=2))
except:
return
def worker():
global q
print 'Worker Initialized'
attributes = ['id', 'text', 'time', 'lat', 'lon']
while True:
responses = q.receive_messages(MessageAttributeNames=attributes)
if len(responses) != 0:
for response in responses:
if response.message_attributes is None:
response.delete()
continue
id = response.message_attributes.get('id').get('StringValue')
text = response.message_attributes.get('text').get(
'StringValue')
time = response.message_attributes.get('time').get(
'StringValue')
lat = response.message_attributes.get('lat').get('StringValue')
lon = response.message_attributes.get('lon').get('StringValue')
try:
natural_language_understanding = NaturalLanguageUnderstandingV1(\
version='2017-02-27',\
username=nlu_creds['username'],\
password=nlu_creds['password']\
)
nlu_response = natural_language_understanding.analyze(\
text=text,\
features=[features.Entities(), features.Keywords(), features.Sentiment()]\
)
sentiment = nlu_response['sentiment']['document']['label']
except Exception as e:
print 'Error:', e
sentiment = 'neutral'
# Send to AWS SNS
notification = {
'id': id,
'text': text,
'time': time,
'lat': lat,
'lon': lon,
'sentiment': sentiment
}
try:
print notification
sns.publish(TargetArn=sns_arn,
Message=json.dumps(
{'default': json.dumps(notification)}))
response.delete()
except Exception as e:
print 'Error:', e
sleep(2)
def extract_sentiment_ner_trec_full(src, dst_dir):
"""
Extracts tweet overall sentiment, sentiment per NER, NERs,
keywords, sentiment per keyword for the full dataset that's read from a
.txt file.
Parameters
----------
src: str - path to dataset.
dst_dir: - directory in which results will be stored.
"""
tweets = read_txt(src)
# Since tweets are ordered according to topic, label them in a
# random order
keys = tweets.keys()
random.shuffle(keys)
for idx, tid in enumerate(keys):
fname = "{}.json".format(tid)
dst = os.path.join(dst_dir, fname)
# If file already exists, data was extracted before and due to
# rate-limiting the rest couldn't be extracted
if not os.path.isfile(dst):
try:
# Extract features for a tweet via Watson
response = natural_language_understanding.analyze(
text=tweets[tid]["text"],
# Get entities and their
features=[
# Overall tweet sentiment
Features.Sentiment(),
# NER detection and sentiment per NER
Features.Entities(sentiment=False),
Features.Keywords(sentiment=False),
])
# Store results in UTF-8 encoding
fname = "{}.json".format(tid)
dst = os.path.join(dst_dir, fname)
with codecs.open(dst, "w", encoding="utf-8") as f:
# https://stackoverflow.com/questions/18337407/saving-utf-8-texts-in-json-dumps-as-utf8-not-as-u-escape-sequence
data = json.dumps(response,
ensure_ascii=False,
encoding='utf8')
f.write(unicode(data))
# Illegal language
except watson_developer_cloud.watson_developer_cloud_service.\
WatsonException:
pass
print "Finished extraction for {} tweets".format(idx + 1)
def featureList(self, tags):
f_list = []
for tag in tags:
if tag == "sentiment":
f_list.append(features.Sentiment())
elif tag == "categories":
f_list.append(features.Categories())
elif tag == "concepts":
f_list.append(features.Concepts())
elif tag == "emotion":
f_list.append(features.Emotion())
elif tag == "entities":
f_list.append(features.Entities())
return f_list
def nlu(text):
response = n.analyze(text=text,
features=[
features.Emotion(),
features.Concepts(),
features.Categories(),
features.Entities(),
features.Keywords(),
features.SemanticRoles(),
features.Relations(),
features.Sentiment()
],
language='en')
return json.dumps(response, indent=2)
def map_feature(name):
feature_name_mappings = {
'keywords': features.Keywords(),
'entities': features.Entities(),
'concepts': features.Concepts(),
'categories': features.Categories(),
'sentiment': features.Sentiment(),
'emotion': features.Emotion()
}
if name in feature_name_mappings:
return feature_name_mappings[name]
else:
print("Invalid feature name")
return None
def getEntity(self, text):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username="******",
password="******",
version="2017-02-27")
response = natural_language_understanding.analyze(
text=text, features=[Features.Entities(sentiment=True)])
output = []
# Look inside data
for word in response["entities"]:
output.append((word['text'], word['type']))
return output
def company_finder(text_in=str):
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username='******',
password='******')
response = natural_language_understanding.analyze(
text=text_in, features=[features.Entities()])
number_of_companis = 0
companis = []
for entity in response["entities"]:
if entity['type'] == 'Company':
number_of_companis += 1
companis.append(entity['text'])
return number_of_companis, companis
def extract_data(text):
# Use Watson's NLU API to extract the keywords, entities and concepts from a text
bm_username = "******"
bm_password = "******"
nlu = watson_developer_cloud.NaturalLanguageUnderstandingV1(
version='2017-02-27', username=bm_username, password=bm_password)
ents = nlu.analyze(text=text,
features=[
features.Entities(),
features.Keywords(),
features.Concepts()
])
ents["tweet"] = text
return ents
def watson_sentiments(url_news):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username="******",
password="******",
version="2017-02-27")
response = natural_language_understanding.analyze(
url=url_news,
features=[
Features.Entities(
# Entities options
sentiment=True,
limit=1)
])
return response
def test_model():
model = request.forms.get('model')
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username="******",
password='******')
response = natural_language_understanding.analyze(text=model,
features=[
features.Entities(),
features.Keywords(),
features.Concepts()
])
return (json.dumps(response, indent=2))
def extract_sentiment_ner_twitter(cleaned, min_annos, dst_dir):
"""
Extracts tweet overall sentiment, sentiment per NER, NERs,
keywords, sentiment per keyword.
Parameters
----------
cleaned: bool - True if only cleaned data should be used (i.e. any
additional labels (and their annotation times) assigned to tweets considered
"Irrelevant" are ignored)
min_annos: int - minimum number of annotators who must've labeled a tweet
for it to be considered.
dst_dir: - directory in which results will be stored.
"""
tweets = read_twitter(cleaned, min_annos)
for idx, tid in enumerate(tweets):
tweet = tweets[tid]
fname = "{}.json".format(tid)
dst = os.path.join(dst_dir, fname)
print tweet["text"]
# If file already exists, data was extracted before and due to
# rate-limiting the rest couldn't be extracted
if not os.path.isfile(dst):
# Extract features for a tweet via Watson
response = natural_language_understanding.analyze(
text=tweet["text"],
# Get entities and their
features=[
# Overall tweet sentiment
Features.Sentiment(),
# NER detection and sentiment per NER
Features.Entities(sentiment=True),
Features.Keywords(sentiment=True),
])
# Store results in UTF-8 encoding
fname = "{}.json".format(tid)
dst = os.path.join(dst_dir, fname)
with codecs.open(dst, "w", encoding="utf-8") as f:
# https://stackoverflow.com/questions/18337407/saving-utf-8-texts-in-json-dumps-as-utf8-not-as-u-escape-sequence
data = json.dumps(response,
ensure_ascii=False,
encoding='utf8')
f.write(unicode(data))
print "Finished extraction for {} tweets".format(idx + 1)
def clasificarDescripcion(pDescripcion):
# if 'VCAP_SERVICES' in os.environ:
# services = json.loads(os.getenv('VCAP_SERVICES'))
with open('AutosClasificados\core\config.json') as json_data_file:
vConfig = json.load(json_data_file)
vAPIUserNLU= vConfig["watsonNLU"]["vAPIUser"]
vAPIPassNLU = vConfig["watsonNLU"]["vAPIPass"]
vAPIVersionNLU = vConfig["watsonNLU"]["vAPIVersion"]
vUmbralMinScore_WNLU = vConfig["watsonNLU"]["vUmbralMinScore_WNLU"]
vUmbralMinDescripcion = vConfig["otros"]["vUmbralMinDescripcion"]
vResultado_NLU = ''
vWatson_NLU = NaturalLanguageUnderstandingV1(username=vAPIUserNLU, password=vAPIPassNLU, version=vAPIVersionNLU)
vListaKeywords = list()
try:
if len(pDescripcion) > vUmbralMinDescripcion:
vResultado_NLU = vWatson_NLU.analyze(
text=pDescripcion,
features=[
Features.Entities(
emotion=True,
sentiment=True,
limit=6
),
Features.Keywords(
emotion=True,
sentiment=True,
limit=6
)
],
language="en"
)
vResultado_NLU = json.loads(json.dumps(vResultado_NLU, indent=2))
if vResultado_NLU['keywords']:
for entitien in vResultado_NLU['entities']:
print(entitien)
for vResultado in vResultado_NLU['keywords']:
print(vResultado)
if vResultado['relevance'] > vUmbralMinScore_WNLU:
vListaKeywords.append(vResultado['text'])
return vListaKeywords
except:
vListaKeywords.append('No hay Keywords disponibles')
return vListaKeywords
def respond_chat(request):
print("respond_chat got called")
global response
global inp
# Getting the last context for the concerned user
last_context = Mess.objects.filter(user=request.user).last().context
# print(last_context.last().created)
# Sending the message to the bot and fetching a response
print("INP------------")
print(inp)
nlu_response = nlu.analyze(
text=inp, features=[features.Entities(),
features.Keywords()])
if (last_context == ""):
response = conversation.message(workspace_id=workspace_id,
message_input={'text': inp})
else:
response = conversation.message(workspace_id=workspace_id,
message_input={'text': inp},
context=eval(last_context))
action = identifier(response, nlu_response)
print("ACTION REQUESTED")
print(action)
# CHECK WHAT THE ACTION IS
# print(type(response['intents']))
# if(response['intents']['intent'][0] == "create_playlist"):
# print("USER WANTS A PLAYLIST MAN")
new_mess = Mess(text=response['output']['text'][0],
user=request.user,
created=timezone.now(),
reality_coefficient=False,
context=repr(response['context']))
new_mess.save()
mess = Mess.objects.all()
response_text = serializers.serialize('json', Mess.objects.all())
return HttpResponse(response_text, content_type='application/json')
def report(self, text):
"""
Returns the Watson Data for a specific text.
"""
# Real Call
payload = self.natural_language_understanding.analyze(
text=text,
features=[
features.Entities(),
features.Keywords(),
features.Emotion()
])
# Fake Call, since we only have limited access to IBM
# payload = self.mock_watson(text)
return payload
def get_sentiment(txt):
response = natural_language_understanding.analyze(
text=txt,
features=[
Features.Entities(
emotion=True,
sentiment=True,
limit=2
),
Features.Keywords(
emotion=True,
sentiment=True,
limit=2
)
]
)
print(json.dumps(response, indent=2))
def analyze(text, threshold=0.5):
text = text.encode('ascii', errors='ignore')
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username='******',
password='******')
response = natural_language_understanding.analyze(text=text,
features=[
features.Entities(),
features.Keywords(),
features.Concepts(),
features.Sentiment()
])
decoder = json.JSONDecoder()
decoded_response = decoder.decode(json.dumps(response, indent=2))
language = decoded_response["language"]
keywords = decoded_response["keywords"]
entities = decoded_response["entities"]
concepts = decoded_response["concepts"]
sentiment = decoded_response["sentiment"]
keywords = sorted(keywords, key=lambda x: -x['relevance'])
keywords = [
keyword for keyword in keywords if keyword['relevance'] >= threshold
]
keywords = [keyword['text'] for keyword in keywords]
entities = sorted(entities, key=lambda x: -x['relevance'])
entities = [
entity for entity in entities if entity['relevance'] >= threshold
]
entities = [(entity['type'], entity['text']) for entity in entities]
concepts = sorted(concepts, key=lambda x: -x['relevance'])
concepts = [
concept for concept in concepts if concept['relevance'] >= threshold
]
concepts = [concept['text'] for concept in concepts]
sentiment = (sentiment['document']['label'],
sentiment['document']['score'])
def main(params):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username=params["username"],
password=params["password"],
version=params["version"])
response = natural_language_understanding.analyze(
url=params["url"],
features=[
Features.Concepts(limit=1),
Features.Entities(limit=1),
Features.Keywords(limit=1),
Features.Categories(),
Features.Emotion(),
Features.Sentiment(),
Features.MetaData(),
Features.Relations(),
Features.SemanticRoles(limit=1)
])
return response
def process(line):
# replace some known utf-8 chars with ascii
line = re.sub("\xe2\x80\x99", "x",
line) # U+2019 (right single quotation mark)
line = re.sub("\xe2\x80\x93", "-", line) # U+2013 (EN-DASH)
# remove the rest of the non-ascii chars
line = re.sub(r'[^\x00-\x7F]+', ' ', line)
if len(line) > 0:
response = client.analyze(text=line, features=[Features.Entities()])
tags = set()
print(json.dumps(response, indent=2))
for entity in response['entities']:
name = entity['text'].encode('ascii', 'ignore')
tags.update(query(name))
return ",".join(tags).encode('utf-8')
else:
return ""
def get_nlu(data):
response = natural_language_understanding.analyze(
text = data,
features=[
Features.Entities(
emotion=True,
sentiment=True,
limit=2),
Features.Keywords(
emotion=True,
sentiment=True,
limit=2),
Features.Emotion(),
]
)
info = json.dumps(response, indent=2)
return response
def execute_watson_request(text):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username=constants.WATSON_USER,
password=constants.WATSON_PASS,
version="2017-02-27")
try:
response = natural_language_understanding.analyze(
text=text,
features=[
features.Concepts(),
features.Categories(),
features.Emotion(),
features.Entities(emotion=True, sentiment=True),
features.Keywords(emotion=True, sentiment=True),
features.Sentiment()
])
return response
except WatsonException as error:
return str(error)
def understand_text(self):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username=nlu_settings.get("username"),
password=nlu_settings.get("password"),
version="2017-02-27")
self.nl_understanding = natural_language_understanding.analyze(
text=self.converted_text,
features=[
Features.Entities(emotion=True, sentiment=True, limit=100),
Features.Keywords(emotion=True, sentiment=True, limit=100),
Features.Categories(),
Features.Concepts(),
Features.Sentiment(),
Features.Emotion(),
# Features.Feature(),
# Features.MetaData(),
Features.Relations(),
Features.SemanticRoles(),
])
return self.nl_understanding
