def getres(file_loc):
with open(file_loc, 'r+') as f:
head = f.readline()
content = f.read()
req = '<html><body><h2>{0}</h2>{1}</body></html>'.format(head, content)
text = head + content
tone_res = tone_analyzer.tone(req, content_type='text/html').get_result()
res = natural_language_understanding.analyze(
html=req,
features=Features(
categories=CategoriesOptions(limit=1),
concepts=ConceptsOptions(limit=5),
keywords=KeywordsOptions(limit=5, sentiment=True, emotion=True),
sentiment=SentimentOptions(),
# entities=EntitiesOptions(limit=5, mentions=True, sentiment=True, emotion=True),
),
).get_result()
sentiment = res["sentiment"]["document"]["score"]
concepts = [(concepts["text"], concepts["relevance"])
for concepts in res["concepts"]]
categories = (res["categories"][0]["label"].split("/"),
res["categories"][0]["score"])
keywords = [(keywords["text"], keywords["relevance"])
for keywords in res["keywords"]]
tones = [(tone["tone_id"], tone["score"])
for tone in tone_res["document_tone"]["tones"]]
return (sentiment, concepts, keywords, tones, text)
def get_keywords_from_url(url):
text = None
if 'reddit' in url:
submission = _reddit.submission(url=url)
if submission.selftext is not None:
text = submission.selftext
url = None
else:
return []
res = _watson_nlu.analyze(
url=url,
text=text,
features=Features(
categories=CategoriesOptions(),
concepts=ConceptsOptions(limit=5)
)).get_result()
kwds = set()
if 'categories' in res:
categories = res['categories']
categories = sorted(categories, key=extract_relevancy, reverse=True)[:10]
for category in categories:
labels = re.split(',| |/', category['label'])
for label in labels:
kwds.add(label)
if 'concepts' in res:
for concept in res['concepts']:
kwds.add(concept['text'])
for stopword in _stopwords:
if stopword in kwds:
kwds.remove(stopword)
return list(kwds)
def watson_nlp_analysis(text):
if text == '': return text
max_limit_one = 10
max_limit_two = 30
naturalLanguageUnderstanding = NaturalLanguageUnderstandingV1(
version='2018-11-16',
iam_apikey=os.environ['WATSON'],
url=
'https://gateway.watsonplatform.net/natural-language-understanding/api'
)
response = naturalLanguageUnderstanding.analyze(
text=text,
features=Features(concepts=ConceptsOptions(limit=max_limit_one),
categories=CategoriesOptions(limit=max_limit_one),
sentiment=SentimentOptions(document=True),
emotion=EmotionOptions(document=True),
entities=EntitiesOptions(emotion=True,
sentiment=True,
limit=max_limit_two),
keywords=KeywordsOptions(
emotion=True,
sentiment=True,
limit=max_limit_two))).get_result()
return response
def makeWikiSection(self, sectiontitle):
print("Accessing IBM Watson for NLP understanding on " + sectiontitle +
" (subtopic of " + self._topic + ")")
response = self.watsonobj.analyze(
text=self._page.section(sectiontitle),
features=Features(concepts=ConceptsOptions(limit=3),
entities=EntitiesOptions(limit=3),
keywords=KeywordsOptions(limit=5),
relations=RelationsOptions(),
semantic_roles=SemanticRolesOptions(limit=3)))
if sectiontitle in wikipedia.search(
sectiontitle) and sectiontitle is not "See also":
return Node("Section",
title=sectiontitle,
content=self._page.section(sectiontitle),
concepts=json.dumps(response["concepts"]),
entities=json.dumps(response["entities"]),
keywords=json.dumps(response["keywords"]),
relations=json.dumps(response["relations"]),
semantic_roles=json.dumps(response["semantic_roles"]),
mainarticleurl=wikipedia.page(self._topic).url)
return Node("Section",
title=sectiontitle,
content=self._page.section(sectiontitle),
concepts=json.dumps(response["concepts"]),
entities=json.dumps(response["entities"]),
keywords=json.dumps(response["keywords"]),
relations=json.dumps(response["relations"]),
semantic_roles=json.dumps(response["semantic_roles"]))
def extractPopularityText(pytrends, natural_language_understanding, text,numberConcepts=4):
### Getting the concepts of the text
response = natural_language_understanding.analyze(
text=text,
features=Features(concepts=ConceptsOptions(limit=numberConcepts)))
#### Parsing the json response
concepts = response["concepts"]
concepts = [k for k in concepts if k['relevance'] > 0.75]
### Google trends for getting the popularity of each concept
kw_list = [k['text'] for k in concepts]
#### Timeframe indicates the period during which we mesure popularity
pytrends.build_payload(kw_list, cat=0, timeframe='today 1-m', geo='',gprop='')
data = pytrends.interest_over_time()
data = data.drop('isPartial', axis=1)
#### Average popularity during the last month
average = data.mean()
### Convert the results to json
dict_averages = average.to_dict()
result = json.dumps(dict_averages)
print(result, '\n')
def understanding(self):
if not self.transcription:
self.transcript()
natural_language_understanding = NaturalLanguageUnderstandingV1(
version='2017-02-27',
username=os.environ['UNDERSTANDING_USERNAME'],
password=os.environ['UNDERSTANDING_PASSWORD'])
self.analysis = natural_language_understanding.analyze(
text=self.transcription['results'][0]['alternatives'][0]
['transcript'],
features=Features(categories=CategoriesOptions(),
concepts=ConceptsOptions(),
emotion=EmotionOptions(),
entities=EntitiesOptions(emotion=True,
sentiment=True,
mentions=True),
keywords=KeywordsOptions(emotion=True,
sentiment=True),
relations=RelationsOptions(),
sentiment=SentimentOptions()))
logger.info('Completed analysis of recorded file')
return self.analysis
def summarizewithkeywords(self, keywords, text, words=None):
try:
concepts = self.watsonobj.analyze(
text=keywords,
features=Features(concepts=ConceptsOptions(limit=3)))
print(
Summarize(" ".join([c['text'] for c in concepts['concepts']]),
text, words))
except Exception:
print(Summarize(" ".join(keywords), text, words))
def get_concepts(text):
try:
response2 = natural_language_understanding.analyze(
text=text, features=Features(concepts=ConceptsOptions(limit=3)))
concepts = []
for concept in response2["concepts"]:
concepts.append(concept["text"])
return concepts
except:
return []
def _concept_finder(self, sentence):
# Uses IBM BlueMix.
try:
response = self.natural_language_understanding.analyze(
text=sentence,
features=Features(concepts=ConceptsOptions(limit=3)))
if response.get("concepts") == []:
return None
return (response.get("concepts")[0]).get("text")
except watson_developer_cloud.watson_service.WatsonApiException:
return None
def _concept_finder(self, sentence):
try:
response = self.nlp_engine.analyze(
text=sentence,
features=Features(concepts=ConceptsOptions(limit=5)))
#print('Response from Concept Finder ->', response)
if response.get("concepts") == []:
return None
return (response.get("concepts")[0]).get("text")
except watson_developer_cloud.watson_service.WatsonApiException:
return None
def call_api(self, content):
try:
response = self.client.analyze(
text=content,
features=Features(entities=EntitiesOptions(),
keywords=KeywordsOptions(),
categories=CategoriesOptions(),
concepts=ConceptsOptions())).get_result()
except WatsonException as exception:
print(exception)
response = {"ibm_exception": str(exception)}
return json.dumps(response, sort_keys=True, indent=4)
def get_news_sentiment(request):
try:
response = natural_language_understanding.analyze(
url=request.GET.get('url'),
features=Features(sentiment=SentimentOptions(),
emotion=EmotionOptions(),
concepts=ConceptsOptions(limit=5),
categories=CategoriesOptions()))
return Response(response)
except:
return Response({"error": 'problem retrieving'})
def __getConcepts(self,
numberConcepts=4,
min_relevance=0.75,
text=None,
url=None):
response = self.natural_language_understanding.analyze(
text=text,
url=url,
features=Features(concepts=ConceptsOptions(limit=numberConcepts)))
concepts = response["concepts"]
concepts = [k for k in concepts if k['relevance'] > 0.75]
return concepts
def queryWatson(headline, story):
return nlu.analyze(text=headline + "; " + story,
language="en",
features=Features(
entities=EntitiesOptions(emotion=False,
sentiment=False,
limit=60),
keywords=KeywordsOptions(emotion=False,
sentiment=False,
limit=60),
categories=CategoriesOptions(limit=60),
concepts=ConceptsOptions(limit=50)))
def get_concepts(self, text=None, url=None):
"""
returns a list of dicts with text, relevance, reference url
"""
if text == None and url == None:
return "Error"
response = naturalLanguageUnderstanding.analyze(
text=text, url=url,
features=Features(concepts=ConceptsOptions())).get_result()
return response['concepts']
def analyze_using_NLU(analysistext):
res = dict()
response = natural_language_understanding.analyze(
text=analysistext,
features=Features(sentiment=SentimentOptions(),
entities=EntitiesOptions(),
keywords=KeywordsOptions(),
emotion=EmotionOptions(),
concepts=ConceptsOptions(),
categories=CategoriesOptions(),
semantic_roles=SemanticRolesOptions()))
res['results'] = response
return json.dumps(res)
def get_keywords(self, sentence):
response = self.natural_language_understanding.analyze(
text=sentence,
return_analyzed_text='True',
features=Features(concepts=ConceptsOptions(),
categories=CategoriesOptions(),
relations=RelationsOptions(),
semantic_roles=SemanticRolesOptions(),
sentiment=SentimentOptions(),
entities=EntitiesOptions(),
keywords=KeywordsOptions())).get_result()
keywords = map(lambda x: (x['text'], x['type']), response['entities'])
return keywords
def analyze(links):
for link in links:
response = natural_language_understanding.analyze(
url=link,
features=Features(
entities=EntitiesOptions(emotion=True,
sentiment=True,
limit=15),
emotion=EmotionOptions(targets=['keyword1', 'keyword2']),
keywords=KeywordsOptions(emotion=True, sentiment=True,
limit=2),
concepts=ConceptsOptions(limit=5),
sentiment=SentimentOptions(targets=['stocks']),
categories=CategoriesOptions()))
def getTopics(self,
numberConcepts=4,
min_relevance=0.75,
text=None,
url=None):
response = self.natural_language_understanding.analyze(
text=text,
url=url,
features=Features(concepts=ConceptsOptions(limit=numberConcepts)))
concepts = response["concepts"]
concepts = [{
'label': k['text'],
'confidence': k['relevance']
} for k in concepts]
return concepts
def analyze_using_NLU(analysistext):
""" Extract results from Watson Natural Language Understanding for each news item
"""
res = dict()
response = natural_language_understanding.analyze(
text=analysistext,
features=Features(
sentiment=SentimentOptions(),
entities=EntitiesOptions(),
keywords=KeywordsOptions(),
emotion=EmotionOptions(),
concepts=ConceptsOptions(),
categories=CategoriesOptions(),
))
res['results'] = response
return res
def interface(Purl):
response = naturalLanguageUnderstanding.analyze(
url=Purl,
features=Features(sentiment=SentimentOptions(),
categories=CategoriesOptions(limit=1),
concepts=ConceptsOptions(limit=1),
entities=EntitiesOptions(limit=1),
metadata=MetadataOptions())).get_result()
result = json.dumps(response, indent=2)
#Sentiment
vLabel = response['sentiment']['document']['label']
score = response['sentiment']['document']['score']
mdTitle = response['metadata']['title']
mdDate = response['metadata']['publication_date']
vScore = math.ceil(float(score) * 100)
#category
try:
for category in response['categories']:
categoryLabel = category['label']
for concept in response['concepts']:
conceptText = concept['text']
conceptRelevance = str(concept['relevance'])
conceptSource = concept['dbpedia_resource']
for entity in response['entities']:
entityText = entity['text']
entityType = entity['type']
except:
vScore = "ERROR"
vLabel = "ERROR"
categoryLabel = "ERROR"
conceptText = "ERROR"
conceptRelevance = "ERROR"
conceptSource = "ERROR"
entityText = "ERROR"
entityType = "ERROR"
mdTitle = "ERROR"
mdDate = "ERROR"
print(response)
return vLabel, vScore, categoryLabel, conceptText, conceptRelevance, conceptSource, entityText, entityType, mdTitle, mdDate
def resultjson():
if request.method == 'POST':
#text1 = request.form
text1 = request.get_json(force=True)
user = natural_language_understanding.analyze(
text=str(text1),
features=Features(entities=EntitiesOptions(emotion=True,
sentiment=True,
limit=2),
relations=RelationsOptions(),
categories=CategoriesOptions(),
semantic_roles=SemanticRolesOptions(),
concepts=ConceptsOptions(limit=3),
keywords=KeywordsOptions(emotion=True,
sentiment=True,
limit=2)))
return jsonify(user)
def understand_transcript(transcription):
nlp_client = NaturalLanguageUnderstandingV1(
version="2017-02-27",
username=os.environ["WATSON_UNDERSTANDING_USERNAME"],
password=os.environ["WATSON_UNDERSTANDING_PASSWORD"],
)
return nlp_client.analyze(
text=transcription["results"][0]["alternatives"][0]["transcript"],
features=Features(
categories=CategoriesOptions(),
concepts=ConceptsOptions(),
emotion=EmotionOptions(),
entities=EntitiesOptions(),
keywords=KeywordsOptions(),
sentiment=SentimentOptions(),
),
)
def conceptExtraction(sentence):
# with open(json_file) as f:
# data = json.load(f)
# data = data[data.keys()[0]]
naturalLanguageUnderstanding = NaturalLanguageUnderstandingV1(
version='2018-09-21',
iam_apikey=apikey,
url=url)
response = naturalLanguageUnderstanding.analyze(
text=sentence,
features=Features(
concepts=ConceptsOptions(
limit=100),
keywords=KeywordsOptions(
limit=100))).get_result()
print(json.dumps(response, indent=2))
def extractPopularityUrl(pytrends, natural_language_understanding, url,numberConcepts=4):
response = natural_language_understanding.analyze(
url=url,
features=Features(concepts=ConceptsOptions(limit=numberConcepts)))
concepts = response["concepts"]
concepts = [k for k in concepts if k['relevance'] > 0.75]
kw_list = [k['text'] for k in concepts]
pytrends.build_payload(kw_list, cat=0, timeframe='today 1-m', geo='', gprop='')
data = pytrends.interest_over_time()
data = data.drop('isPartial', axis=1)
average = data.mean()
dict_averages = average.to_dict()
result = json.dumps(dict_averages)
print(result, '\n')
def get_concepts(self, user_input, max_number_of_concepts=3):
'''
Extract concepts from a given string
:param str user_input:
:param int max_number_of_concepts:
:return: a list of concepts(str)
'''
result = []
response = self.nlu.analyze(text=user_input,
features=Features(concepts=ConceptsOptions(
limit=max_number_of_concepts)),
language='en')
if self.debug_mode:
print(json.dumps(response, indent=2))
concepts = response.get('concepts')
if len(concepts) != 0:
for concept in concepts:
result.append(concept.get('text'))
return result
def _parallel_NlU(self, text):
# A Function to call Watson Natural Language Understanding
if self.config['keywords']:
keyword_option = KeywordsOptions(limit=self.config['keyword_limit'])
else:
keyword_option = None
if self.config['concepts']:
concepts_option = ConceptsOptions(
limit=self.config['concept_limit'])
else:
concepts_option = None
try:
results = self.model.analyze(
text=text,
features=Features(
concepts=concepts_option,
keywords=keyword_option),
language='en'
)
json_results = results.get_result()
our_concepts = []
for concept in json_results['concepts']:
our_concepts.append(concept['text'])
our_keywords = []
for keyword in json_results['keywords']:
our_keywords.append(keyword['text'])
self.lock.acquire()
self.concepts = self.concepts + our_concepts
self.keywords = self.keywords + our_keywords
self.lock.release()
except Exception as e:
print(str(e))
def nluRun(txt):
response = natural_language_understanding.analyze(
text=txt,
features=Features(entities=EntitiesOptions(emotion=True,
sentiment=True,
limit=3),
keywords=KeywordsOptions(emotion=True,
sentiment=True,
limit=3),
concepts=ConceptsOptions(limit=3)))
#Prints the relevant texts and scores, len() is used because earlier it looped each tweet individually then we
#compressed them all to one string
for x in range(0, len(response["concepts"])):
# print(x)
con = response["concepts"][x]["text"]
rel = response["concepts"][x]["relevance"]
# print(response["concepts"][x]["text"])
# print(response["concepts"][x]["relevance"])
sendArray = {'Concept': con, 'Relevance': rel}
print(sendArray)
def get_data(request, ):
# response = watsonResponse
# return JsonResponse(response)
text = "A Beginner’s Guide to Sous Vide Cooking- Kitchen Conundrums with Thomas JosephA Recap of Bill Burr's Best Interview Moments of 2018 Sources:- Sara Silverman:- https://www.youtube.com/watch?v=i-VU25UfHWM Comic Con:- https://www.youtube.com/watch?v=JOVWum1vv4Y&t=8s The Herd...ABC NewsAcaiadam22Adam22 GOES IN on Corny Rapper who paid to be on WorldstarAden FilmsAmerican Football - TopicAndrea Savage on Husband's Tank Top, Daughter & Her Mom's Bizarre Holiday TraditionsApple Killed the Mac Mini.April Fools' Day Pranks with Mark RoberAwkward Bill Burr vs Sarah Silverman InterviewBaked Lemon Chicken with Garlic Lemon Cream Sauce | Oven Baked Chicken RecipeBaked Potato Puffs - Food WishesBaking - TopicBash: How can that come out of Sarah Sanders' mouth?Beastie Boys, Nas - Too Many RappersBeastieBoysBernie Sanders Assesses The 2020 Presidential FieldBest Ever Food Review ShowBest web features of 2018: Part 2/4 - HTTP203Bill Burr | Best of 2018 | A Year In ReviewBinging with BabishBinging with BabishBinging with BabishBINGING WITH BABISH S1 • E78BINGING WITH BABISH S2 • E30Binging with Babish: Chateaubriand Steak from The MatrixBinging with Babish: Roast Beast from How The Grinch Stole ChristmasBinging with Babish: Seinfeld Volume IIBlockchain Takes ManhattanBloombergBon AppétitBoy's emotional Christmas surprise goes viralBrexit (2019) | Official Trailer | HBOBrexit Update - UK Version: Last Week Tonight with John Oliver (HBO)BroadlyBroncos vs. 49ers Week 14 Highlights | NFL 2018BuzzFeedVideoBuzzFeedVideo viewers also watch...Cal McKinley - Go LocalCan a Millennial Troll Survive NATO's Biggest War Games?Cardi B Carpool KaraokeCarFactionCars - TopicCFP RankingsCHAMPAIGN ILL S1 • E1Champaign ILL - Ep 1 “A Gangster Way To Start Your Day”Check out original movies and series from YouTube Creators and moreChicken Noodle Soup - How to Make Classic Chicken Noodle SoupChinese Girl Visits Amish Country - She Was Shocked!CHRISTMAS RECIPE: Honey Glazed Ham With Pear & Saffron ChutneyCNNCNNCNNCNNCNNCNNCNNCNNCobra KaiCOBRA KAI S1 • E1Cobra Kai Ep 1 - “Ace Degenerate” - The Karate Kid Saga ContinuesComedians 'R' GoComedy - TopicComedy CentralComedy Central Stand-UpComedy UniversityComedy UniversityComplexComputerphileCONAN On TBS Returns January 22ndContinue watchingCooking - TopicCS50CS50 Lecture by Steve BallmerDALLAS & ROBO S1 • E1Daniel Solves Your Local Twissues - Tosh.0Day In The Life Of A Software Engineer | Weekend EditionDMX Ends 6ix9ine With Insane FreestyleDoes Mick Mulvaney Like Donald Trump? 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She was fascinated by their way of life, and how they don'...Hip Hop Music - TopicHomemade Meatloaf Recipe - Laura Vitale - Laura in the Kitchen Episode 552HOW - TO S1 • E2How Newark Got Lead In Its Water, And What It Means For The Rest Of America (HBO)How to cook a ALL AMERICAN THANKSGIVINGHow to cook a CHRISTMAS FEASTHow to cook a HANGOVER CURE FEASTHow to cook a SUPER SAIYAN FEASTHow To Cook the Perfect Prime Rib RoastHow to Make Danish Christmas Rice PuddingHow To Make Pot Au Feu: the mother recipe of French soups ( Tutorial for beginners)How To Make Scones | Jamie Oliver | ADI Design A Website In Less Than 1 Hour! | Web Design Challenge | Web Design Guide | mmtutsInstant Pot Roast (Best Ever - Literally)Is the Instant Pot Worth It? — The Kitchen Gadget Test ShowJamie OliverJason Momoa Hasn't Seen Aquaman Yet! | The Graham Norton ShowJeff Ross & David Attell Roast Kimmel AudienceJeff Ross Talks to Mexican Immigrants Deported from America - Jeff Ross Roasts the BorderJerry Seinfeld: Kevin Hart Is ‘Going To Be Fine’ After Oscars Fallout | TODAYJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy Kimmel LiveJimmy O. 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(sound check Q) - ComputerphileWORLDSTARHIPHOPWORTH IT S5 • E8YouTube OriginalsYouTube OriginalsYouTube Originals"
naturalLanguageUnderstanding = NaturalLanguageUnderstandingV1(
version='2018-11-16',
iam_apikey='',
url='https://gateway.watsonplatform.net/natural-language-understanding/api')
response = naturalLanguageUnderstanding.analyze(
text= text,
features=Features(
concepts=ConceptsOptions(limit=10),
categories=CategoriesOptions(limit=10),
sentiment=SentimentOptions(document=True),
emotion=EmotionOptions(document=True),
entities=EntitiesOptions(emotion=True, sentiment=True, limit=30),
keywords=KeywordsOptions(emotion=True, sentiment=True, limit=30))).get_result()
print(json.dumps(response, indent=2))
return JsonResponse(response)
def nlp_ibm(text):
natural_language_understanding = NaturalLanguageUnderstandingV1(
username='******', password='******', version='2018-03-16')
if text:
try:
responses = natural_language_understanding.analyze(
text=text,
return_analyzed_text=False,
features=Features(categories=CategoriesOptions(),
concepts=ConceptsOptions(limit=1),
emotion=EmotionOptions(),
sentiment=SentimentOptions()))
response = [
responses["usage"]["text_characters"],
responses["sentiment"]["document"]["label"],
responses["sentiment"]["document"]["score"]
]
except Exception as e:
print(e)
response = []
else:
response = []
return response
