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 analyse(self, **params):
logger.debug("emotionService with params {}".format(params))
filename = params.get("i", None)
## FILE MANIPULATIONS ------------------------------- \
if validators.url(filename):
filename = self._download_file(saveFolder = self._storage_path, url = filename)
else:
filename = os.path.join(self._storage_path,filename)
logger.info("{} {}".format(datetime.now(), filename))
if not os.path.isfile(filename):
raise Error("File %s does not exist" % filename)
## EXTRACTING FEATURES ------------------------------- \
feature_set = self._extract_features(filename, convert=True)
# self._remove_file(filename)
## GENERATING OUTPUT --------------------------------- \
response = Results()
entry = Entry()
entry['filename'] = os.path.basename(filename)
emotionSet = EmotionSet()
emotionSet.id = "Emotions"
emotion1 = Emotion()
for dimension in self._dimensions:
emotion1[ self._centroid_mappings[dimension] ] = 5*(1+feature_set[dimension])
emotionSet.onyx__hasEmotion.append(emotion1)
entry.emotions = [emotionSet,]
response.entries.append(entry)
return response
def analyse(self, **params):
logger.debug("Analysing with params {}".format(params))
text_input = params.get("input", None)
text=self._my_preprocessor(text_input)
feature_text=self._extract_features(text)
response = Results()
entry = Entry(id="Entry",
text=text_input)
emotionSet = EmotionSet(id="Emotions0")
emotions = emotionSet.onyx__hasEmotion
for i in feature_text:
emotions.append(Emotion(onyx__hasEmotionCategory=self._wnaffect_mappings[i],
onyx__hasEmotionIntensity=feature_text[i]))
entry.emotions = [emotionSet]
response.entries.append(entry)
return response
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
