def test_results(self):
r = Results()
e = Entry()
e.nif__isString = "Results test"
r.entries.append(e)
r.id = ":test_results"
r.validate()
def test_results(self):
r = Results()
e = Entry()
e.nif__isString = "Results test"
r.entries.append(e)
r.id = ":test_results"
r.validate()
def test_entries(self):
e = Entry()
self.assertRaises(jsonschema.ValidationError, e.validate)
e.nif__isString = "this is a test"
e.nif__beginIndex = 0
e.nif__endIndex = 10
e.validate()
def analyse(self, *args, **kwargs):
self.sh['counter'] = self.sh['counter'] + 1
e = Entry()
e.nif__isString = self.sh['counter']
r = Results()
r.entries.append(e)
return r
def analyse(self, **params):
logger.debug(
"SentimentAnalysisDL Analysing with params {}".format(params))
text_input = params.get("input", None)
# st = datetime.now()
text = self.cleanTweet(text_input)
# logger.info("{} {}".format(datetime.now() - st, "tweet cleaned"))
X_test = self.convert_text_to_vector([text], self._tokenizer)
y_pred = self.classify(X_test)
response = Results()
entry = Entry()
_mapping_labels = {0: 'positive', 1: 'negative', 2: 'neutral'}
_mapping_values = {0: "1", 1: "-1", 2: "0"}
for sentence, y_i in zip([text], y_pred):
sentiment = Sentiment()
sentiment['marl:hasPolarity'] = _mapping_labels[y_i]
sentiment["marl:polarityValue"] = _mapping_values[y_i]
entry.sentiments.append(sentiment)
entry.nif__isString = text_input
response.entries.append(entry)
return response
def test_entries(self):
e = Entry()
self.assertRaises(jsonschema.ValidationError, e.validate)
e.nif__isString = "this is a test"
e.nif__beginIndex = 0
e.nif__endIndex = 10
e.validate()
def analyse(self, *args, **kwargs):
self.sh['counter'] = self.sh['counter'] + 1
e = Entry()
e.nif__isString = self.sh['counter']
r = Results()
r.entries.append(e)
return r
def analyse(self, **params):
logger.debug("SuggestionMiningDL Analysing with params {}".format(params))
text_input = params.get("input", None)
st = datetime.now()
text_sentences = self.cleanTweet(text_input) #[self.cleanTweet(sentence) for sentence in self.split_into_sentences(text_input)]
#X_test = [ self.convert_text_to_vector(sentence, self._tokenizer) for sentence in text_sentences]
X_test = self.convert_text_to_vector([text_sentences], self._tokenizer)
y_pred = self.classify(X_test)
logger.info("{} {}".format(datetime.now() - st, "tweet analysed, predicted: "+str(y_pred)))
response = Results()
entry = Entry()
entry.nif__isString = text_input
_mapping_labels = {0:False, 1:True}
for sentence, y_i in zip([text_sentences], y_pred):
suggestion = Suggestion()
suggestion['hasSuggestion'] = _mapping_labels[y_i]
suggestion["nif:beginIndex"] = 0
suggestion["nif:endIndex"] = len(sentence)
suggestion["nif:anchorOf"] = sentence
entry.suggestions.append(suggestion)
response.entries.append(entry)
return response
def analyse(self, **params):
logger.debug("Hashtag SVM Analysing with params {}".format(params))
text_input = params.get("input", None)
self.ESTIMATOR = params.get("estimator", 'LinearSVC')
# EXTRACTING FEATURES
text = self._text_preprocessor(text_input)
X = self._convert_text_to_vector(text=text, text_input=text_input, Dictionary=self._Dictionary)
feature_text = self._extract_features(X=X, classifiers=self._classifiers, estimator=self.ESTIMATOR)
# GENERATING RESPONSE
response = Results()
entry = Entry()
entry.nif__isString = text_input
emotionSet = EmotionSet()
emotionSet.id = "Emotions"
if self.ESTIMATOR == 'SVC':
emotionSet.onyx__maxIntensityValue = float(100.0)
emotion1 = Emotion()
for dimension in ['V','A','D']:
weights = [feature_text[i] for i in feature_text if (i != 'surprise')]
if not all(v == 0 for v in weights):
value = np.average([self.centroids[i][dimension] for i in feature_text if (i != 'surprise')], weights=weights)
else:
value = 5.0
emotion1[self.centroid_mappings[dimension]] = value
emotionSet.onyx__hasEmotion.append(emotion1)
for i in feature_text:
if(self.ESTIMATOR == 'SVC'):
emotionSet.onyx__hasEmotion.append(Emotion(
onyx__hasEmotionCategory=self.wnaffect_mappings[i],
onyx__hasEmotionIntensity=feature_text[i]))
else:
if(feature_text[i] > 0):
emotionSet.onyx__hasEmotion.append(Emotion(
onyx__hasEmotionCategory=self.wnaffect_mappings[i]))
entry.emotions = [emotionSet,]
response.entries.append(entry)
return response
def test_template(self):
r = Results()
e = Entry()
e.nif__isString = 'testing the template'
sent = Sentiment()
sent.polarity = 'marl:Positive'
r.entries.append(e)
e.sentiments.append(sent)
template = ('{% for entry in entries %}'
'{{ entry["nif:isString"] | upper }}'
',{{entry.sentiments[0]["marl:hasPolarity"].split(":")[1]}}'
'{% endfor %}')
res = r.serialize(template=template)
assert res == 'TESTING THE TEMPLATE,Positive'
def test_template(self):
r = Results()
e = Entry()
e.nif__isString = 'testing the template'
sent = Sentiment()
sent.polarity = 'marl:Positive'
r.entries.append(e)
e.sentiments.append(sent)
template = (
'{% for entry in entries %}'
'{{ entry["nif:isString"] | upper }}'
',{{entry.sentiments[0]["marl:hasPolarity"].split(":")[1]}}'
'{% endfor %}')
res = r.serialize(template=template)
assert res == 'TESTING THE TEMPLATE,Positive'
def analyse(self, **params):
logger.debug(
"wassaRegression LSTM Analysing with params {}".format(params))
st = datetime.now()
text_input = params.get("input", None)
text = self._text_preprocessor(text_input)
self.ESTIMATOR = params.get("estimator", 'LSTM')
if self.ESTIMATOR == 'LSTM':
X_lstm = self._lists_to_vectors(text=text)
feature_text = self._extract_features(X_lstm)
elif self.ESTIMATOR == 'averaged':
X_lstm = self._lists_to_vectors(text=text)
X_svr = self._convert_text_to_vector(text=text,
text_input=text_input)
feature_text_lstm = self._extract_features(X_lstm)
feature_text_svr = self._extract_features_svr(X_svr)
feature_text = {
emo: np.mean([feature_text_lstm[emo], feature_text_svr[emo]])
for emo in self._emoNames
}
else:
X_svr = self._convert_text_to_vector(text=text,
text_input=text_input)
feature_text = self._extract_features_svr(X_svr)
logger.info("{} {}".format(datetime.now() - st, "string analysed"))
response = Results()
entry = Entry()
entry.nif__isString = text_input
emotionSet = EmotionSet()
emotionSet.id = "Emotions"
emotionSet.onyx__maxIntensityValue = float(100.0)
emotion1 = Emotion()
for dimension in ['V', 'A', 'D']:
weights = [feature_text[i] for i in feature_text]
if not all(v == 0 for v in weights):
value = np.average(
[self.centroids[i][dimension] for i in feature_text],
weights=weights)
else:
value = 5.0
emotion1[self.centroid_mappings[dimension]] = value
emotionSet.onyx__hasEmotion.append(emotion1)
for i in feature_text:
emotionSet.onyx__hasEmotion.append(
Emotion(onyx__hasEmotionCategory=self.wnaffect_mappings[i],
onyx__hasEmotionIntensity=float(feature_text[i]) *
emotionSet.onyx__maxIntensityValue))
entry.emotions = [
emotionSet,
]
response.entries.append(entry)
return response
def analyse(self, **params):
logger.debug("Hashtag LSTM Analysing with params {}".format(params))
text_input = params.get("input", None)
self._ESTIMATION = params.get("estimation", 'Probabilities')
# EXTRACTING FEATURES
text = self._text_preprocessor(text_input)
X = self._lists_to_vectors(text=text)
feature_text = self._extract_features(X=X)
# GENERATING RESPONSE
response = Results()
entry = Entry()
entry.nif__isString = text_input
emotionSet = EmotionSet()
emotionSet.id = "Emotions"
if self._ESTIMATION == 'Probabilities':
emotionSet.onyx__maxIntensityValue = float(100.0)
emotion1 = Emotion()
for dimension in ['V', 'A', 'D']:
weights = [
feature_text[i] for i in feature_text if (i != 'surprise')
]
if not all(v == 0 for v in weights):
value = np.average([
self.centroids[i][dimension]
for i in feature_text if (i != 'surprise')
],
weights=weights)
else:
value = 5.0
emotion1[self.centroid_mappings[dimension]] = value
emotionSet.onyx__hasEmotion.append(emotion1)
for i in feature_text:
if self._ESTIMATION == 'Probabilities':
emotionSet.onyx__hasEmotion.append(
Emotion(onyx__hasEmotionCategory=self.wnaffect_mappings[i],
onyx__hasEmotionIntensity=float(feature_text[i]) *
100))
elif self._ESTIMATION == 'Classes':
if feature_text[i] > 0:
emotionSet.onyx__hasEmotion.append(
Emotion(onyx__hasEmotionCategory=self.
wnaffect_mappings[i]))
#onyx__hasEmotionIntensity=int(feature_text[i])))
entry.emotions = [
emotionSet,
]
response.entries.append(entry)
return response
