def indico_batch_ner():
"""another ONE-OFF method to call the indico.io API to batch NER 18192 texts
"""
with open('sentiments.csv', 'wb') as f:
texts = []
writer = csv.writer(f)
with open('texts/filenames.txt', 'r') as filenames:
fn_list = map(str.strip, [filename for filename in filenames])
fn_list = map(lambda x: 'texts/texts/' + x, fn_list)
for fn in fn_list:
texts.append(get_texts(fn)) # returns TextMessage object
texts = [item for sublist in texts for item in sublist]
with open('indico_ner_errors.txt', 'w') as error_log:
for text in texts:
sentiment_result = None
try:
sentiment_result = named_entities(text.body.encode(),
api_key=INDICO_API_KEY)
except BaseException as e:
error_log.write(str(e))
finally:
writer.writerow([
unicode(s).encode('utf-8') for s in [
text.msg_id, text.posix,
repr(text.sent), text.body,
repr(text.mentions), sentiment_result
]
])
def analysis(data):
sentiment = ind.sentiment_hq(data)
tags = sort(ind.text_tags(data))
languages = sort(ind.language(data))
politics = sort(ind.political(data))
keywords = sort(ind.keywords(data))
names = sort(ind.named_entities(data))
print "Sentiment", sentiment
print "\n\n\nTags"
for t in tags:
print t[0], float(t[1]) * 100
print "\n\n\nLanguages"
for l in languages:
print l[0], float(l[1]) * 100
print "\n\n\nPolitical"
for p in politics:
print p[0], float(p[1]) * 100
print "\n\nkeywords"
for k in keywords:
print k[0], float(k[1]) * 100
def test_named_entities(self):
text = "London Underground's boss Mike Brown warned that the strike ..."
expected_entities = ("London Underground", "Mike Brown")
expected_keys = set(["categories", "confidence"])
entities = named_entities(text)
for entity in expected_entities:
assert entity in expected_entities
assert not (set(entities[entity]) - expected_keys)
def entityMatch(message): ne = indicoio.named_entities(message) for e in ne.keys(): if ne[e]["confidence"] > .8: if ne[e]["categories"]["organization"] > .5: if e in entities: return e return "None"
def score(self, slide_length, window_length, AItype='tags'):
self.parse(slide_length, window_length)
if AItype == 'tags':
self.scores['tags'] = [indicoio.text_tags(i) for i in self.strings]
elif AItype == 'keywords':
self.scores['keywords'] = [indicoio.keywords(i) for i in self.strings]
elif AItype == 'names':
self.scores['names'] = [indicoio.named_entities(i) for i in self.strings]
else:
raise Exception('Warning: {} not a valid category'.format(category))
def indico_batch_ner():
"""another ONE-OFF method to call the indico.io API to batch NER 18192 texts
"""
with open('sentiments.csv', 'wb') as f:
texts = []
writer = csv.writer(f)
with open('texts/filenames.txt', 'r') as filenames:
fn_list = map(str.strip, [filename for filename in filenames])
fn_list = map(lambda x: 'texts/texts/' + x, fn_list)
for fn in fn_list:
texts.append(get_texts(fn)) # returns TextMessage object
texts = [item for sublist in texts for item in sublist]
with open('indico_ner_errors.txt', 'w') as error_log:
for text in texts:
sentiment_result = None
try:
sentiment_result = named_entities(text.body.encode(), api_key=INDICO_API_KEY)
except BaseException as e:
error_log.write(str(e))
finally:
writer.writerow([unicode(s).encode('utf-8') for s in
[text.msg_id, text.posix, repr(text.sent),
text.body, repr(text.mentions), sentiment_result]])
def entity_extraction(self, text):
self.named_entities = named_entities(text, threshold=0)
#fd = open("BBC.txt", "r")
#string1 += fd.read()
string1 = string1.replace("\xe2\x80\x9c", "\"")
string1 = string1.replace("\xe2\x80\x9d", "\"")
string1 = string1.replace("\xe2\x80\x99", "\'")
keywordList = []
tagList = []
entityList = []
myList = string1.split("\n", size)
for x in range(0, size):
keywordList.append(indicoio.keywords(myList[x], top_n=10,
independent=True))
tagList.append(indicoio.text_tags(myList[x], threshold=.05))
entityList.append(indicoio.named_entities(myList[x]))
#print indicoio.text_tags(myList[x], threshold=.1)
#print indicoio.keywords(myList[x], top_n=6, independent=True)
## build 2-d array of weights
matrix = [[0 for x in range(size)] for x in range(size)]
for x in range(0, size):
for y in range(0, size):
matrix[x][y] = 1000 * compareKeywords(
keywordList[x], keywordList[y]) * compareTags(
tagList[x], tagList[y]) * compareEntities(
entityList[x], entityList[y])
#print str(x) + " " + str(y) + " " + str(matrix[x][y])
def findNames(inputString):
return indicoio.named_entities(inputString)
def fetch_named_entities(self):
properNouns = indicoio.named_entities(self.data)
print("\nproperNouns: ", properNouns)
nounDict = {k: v['confidence'] for k, v in properNouns.items()}
self.final_json['keywords'].update(
sorted(nounDict, key=nounDict.get, reverse=True)[:5])
def get_entities():
if request.method == 'POST':
data = dict(request.form)['data_to_analyze']
return json.dumps({
'keywords': indicoio.named_entities(data)
})
def fetch_named_entities(self):
properNouns = indicoio.named_entities(self.data)
print("\nproperNouns: ", properNouns)
nounDict = {k:v['confidence'] for k,v in properNouns.items()}
self.final_json['keywords'].update(sorted(nounDict, key=nounDict.get, reverse=True)[:5])
