def analyse(self, **params):
lang = params.get("language", "auto")
res = requests.post("http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({"language": lang,
"data": [{"text": params["input"]}]
}
)
)
p = params.get("prefix", None)
response = Results(prefix=p)
polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
entry = Entry(id="Entry0",
nif__isString=params["input"])
sentiment = Sentiment(id="Sentiment0",
prefix=p,
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
sentiment.prov__wasGeneratedBy = self.id
entry.sentiments = []
entry.sentiments.append(sentiment)
entry.language = lang
response.entries.append(entry)
return response
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):
lang = params.get("language", "auto")
res = requests.post(
"http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({
"language": lang,
"data": [{
"text": params["input"]
}]
}))
p = params.get("prefix", None)
response = Response(prefix=p)
polarity_value = self.maxPolarityValue * int(
res.json()["data"][0]["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
entry = Entry(id="Entry0", text=params["input"], prefix=p)
opinion = Opinion(id="Opinion0",
prefix=p,
hasPolarity=polarity,
polarityValue=polarity_value)
opinion["prov:wasGeneratedBy"] = self.id
entry.opinions.append(opinion)
entry.language = lang
response.entries.append(entry)
return response
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 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):
lang = params.get("language", "auto")
res = requests.post("http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({"language": lang,
"data": [{"text": params["input"]}]
}
)
)
p = params.get("prefix", None)
response = Response(prefix=p)
polarity_value = self.maxPolarityValue*int(res.json()["data"][0]
["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
entry = Entry(id="Entry0",
text=params["input"],
prefix=p)
opinion = Opinion(id="Opinion0",
prefix=p,
hasPolarity=polarity,
polarityValue=polarity_value)
opinion["prov:wasGeneratedBy"] = self.id
entry.opinions.append(opinion)
entry.language = lang
response.entries.append(entry)
return response
def test_results(self):
r = Results()
e = Entry()
e.nif__isString = "Results test"
r.entries.append(e)
r.id = ":test_results"
r.validate()
def analyse(self, **params):
lang = params.get("language", "auto")
res = requests.post(
"http://www.sentiment140.com/api/bulkClassifyJson",
json.dumps({
"language": lang,
"data": [{
"text": params["input"]
}]
}))
p = params.get("prefix", None)
response = Results(prefix=p)
polarity_value = self.maxPolarityValue * int(
res.json()["data"][0]["polarity"]) * 0.25
polarity = "marl:Neutral"
neutral_value = self.maxPolarityValue / 2.0
if polarity_value > neutral_value:
polarity = "marl:Positive"
elif polarity_value < neutral_value:
polarity = "marl:Negative"
entry = Entry(id="Entry0", nif__isString=params["input"])
sentiment = Sentiment(id="Sentiment0",
prefix=p,
marl__hasPolarity=polarity,
marl__polarityValue=polarity_value)
sentiment.prov__wasGeneratedBy = self.id
entry.sentiments = []
entry.sentiments.append(sentiment)
entry.language = lang
response.entries.append(entry)
return response
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_id(self):
""" Adding the id after creation should overwrite the automatic ID
"""
r = Entry(_auto_id=True)
j = r.jsonld()
assert '@id' in j
r.id = "test"
j2 = r.jsonld()
assert j2['@id'] == 'test'
assert 'id' not in j2
def test_id(self):
""" Adding the id after creation should overwrite the automatic ID
"""
r = Entry()
j = r.jsonld()
assert '@id' in j
r.id = "test"
j2 = r.jsonld()
assert j2['@id'] == 'test'
assert 'id' not in j2
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 analyse(self, **params):
logger.debug("Analysing with params {}".format(params))
text_input = params.get("input", None)
text=self._my_preprocessor(text_input)
dictionary={}
lang = params.get("language", "auto")
if lang == 'es':
with open(self.local_path+self.anew_path_es,'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary[row[2]]={}
dictionary[row[2]]['V']=row[4]
dictionary[row[2]]['A']=row[6]
dictionary[row[2]]['D']=row[8]
else:
with open(self.local_path+self.anew_path_en,'rb') as tabfile:
reader = csv.reader(tabfile, delimiter='\t')
for row in reader:
dictionary[row[0]]={}
dictionary[row[0]]['V']=row[2]
dictionary[row[0]]['A']=row[4]
dictionary[row[0]]['D']=row[6]
feature_set=self._extract_features(text,dictionary,lang)
p = params.get("prefix", None)
response = Response(prefix=p)
entry = Entry(id="Entry",
text=text_input,
prefix=p)
emotions = EmotionSet(id="Emotions0")
emotion1 = Emotion(id="Emotion0")
emotion1["onyx:hasEmotionCategory"] = self.emotions_ontology[feature_set['emotion']]
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence"] = feature_set['V']
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal"] = feature_set['A']
emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance"] = feature_set['D']
emotions.emotions.append(emotion1)
entry.emotionSets.append(emotions)
entry.language = lang
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 analyse_entry(self, entry, params):
chunker_type = params["delimiter"]
original_text = entry['nif:isString']
if chunker_type == "sentence":
tokenizer = PunktSentenceTokenizer()
if chunker_type == "paragraph":
tokenizer = LineTokenizer()
chars = list(tokenizer.span_tokenize(original_text))
for i, chunk in enumerate(tokenizer.tokenize(original_text)):
print(chunk)
e = Entry()
e['nif:isString'] = chunk
if entry.id:
e.id = entry.id + "#char={},{}".format(chars[i][0], chars[i][1])
yield e
def test_turtle(self):
"""Any model should be serializable as a turtle file"""
ana = EmotionAnalysis()
res = Results()
res.analysis.append(ana)
entry = Entry(text='Just testing')
eSet = EmotionSet()
emotion = Emotion()
entry.emotions.append(eSet)
res.entries.append(entry)
eSet.onyx__hasEmotion.append(emotion)
eSet.prov__wasGeneratedBy = ana.id
triples = ('ana a :Analysis',
'entry a :entry',
' nif:isString "Just testing"',
' onyx:hasEmotionSet eSet',
'eSet a onyx:EmotionSet',
' prov:wasGeneratedBy ana',
' onyx:hasEmotion emotion',
'emotion a onyx:Emotion',
'res a :results',
' me:AnalysisInvoloved ana',
' prov:used entry')
t = res.serialize(format='turtle')
print(t)
g = rdflib.Graph().parse(data=t, format='turtle')
assert len(g) == len(triples)
def test(self):
params = dict()
results = list()
for i in range(100):
res = next(self.analyse_entry(Entry(nif__isString="Hello"), params))
res.validate()
results.append(res.emotions[0]['onyx:hasEmotion'][0]['onyx:hasEmotionCategory'])
def test_serializable(self):
r = Results()
e = Entry()
r.entries.append(e)
d = r.serializable()
assert d
assert d['entries']
def test(self, *args, **kwargs):
results = list()
params = {
'algo': 'emotion-wnaffect',
'intype': 'direct',
'expanded-jsonld': 0,
'informat': 'text',
'prefix': '',
'plugin_type': 'analysisPlugin',
'urischeme': 'RFC5147String',
'outformat': 'json-ld',
'i': 'Hello World',
'input': 'Hello World',
'conversion': 'full',
'language': 'en',
'algorithm': 'emotion-wnaffect'
}
self.activate()
texts = {
'I hate you': 'anger',
'i am sad': 'sadness',
'i am happy with my marks': 'joy',
'This movie is scary': 'negative-fear'
}
for text in texts:
response = next(
self.analyse_entry(Entry(nif__isString=text),
self.activity(params)))
expected = texts[text]
emotionSet = response.emotions[0]
max_emotion = max(emotionSet['onyx:hasEmotion'],
key=lambda x: x['onyx:hasEmotionIntensity'])
assert max_emotion['onyx:hasEmotionCategory'] == expected
def test_jsonld(self):
prueba = {"id": "test", "analysis": [], "entries": []}
r = Results(**prueba)
print("Response's context: ")
pprint(r._context)
assert r.id == "test"
j = r.jsonld(with_context=True)
print("As JSON:")
pprint(j)
assert ("@context" in j)
assert ("marl" in j["@context"])
assert ("entries" in j["@context"])
assert (j["@id"] == "test")
assert "id" not in j
r6 = Results(**prueba)
e = Entry({"@id": "ohno", "nif:isString": "Just testing"})
r6.entries.append(e)
logging.debug("Reponse 6: %s", r6)
assert ("marl" in r6._context)
assert ("entries" in r6._context)
j6 = r6.jsonld(with_context=True)
logging.debug("jsonld: %s", j6)
assert ("@context" in j6)
assert ("entries" in j6)
assert ("analysis" in j6)
resp = r6.flask()
received = json.loads(resp.data.decode())
logging.debug("Response: %s", j6)
assert (received["entries"])
assert (received["entries"][0]["nif:isString"] == "Just testing")
assert (received["entries"][0]["nif:isString"] != "Not testing")
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 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 test_serializable(self):
r = Results()
e = Entry()
ent = Entity()
e.entities.append(ent)
r.entries.append(e)
d = r.serializable()
assert d
assert d['entries']
assert d['entries'][0]['entities']
def analyse_entry(self, entry, activity):
yield entry
chunker_type = activity.params["delimiter"]
original_text = entry['nif:isString']
if chunker_type == "sentence":
tokenizer = PunktSentenceTokenizer()
if chunker_type == "paragraph":
tokenizer = LineTokenizer()
chars = list(tokenizer.span_tokenize(original_text))
if len(chars) == 1:
# This sentence was already split
return
for i, chunk in enumerate(chars):
start, end = chunk
e = Entry()
e['nif:isString'] = original_text[start:end]
if entry.id:
e.id = entry.id + "#char={},{}".format(start, end)
yield e
def analyse_entry(self, entry, params):
yield entry
chunker_type = params["delimiter"]
original_text = entry['nif:isString']
if chunker_type == "sentence":
tokenizer = PunktSentenceTokenizer()
if chunker_type == "paragraph":
tokenizer = LineTokenizer()
chars = list(tokenizer.span_tokenize(original_text))
if len(chars) == 1:
# This sentence was already split
return
for i, chunk in enumerate(chars):
start, end = chunk
e = Entry()
e['nif:isString'] = original_text[start:end]
if entry.id:
e.id = entry.id + "#char={},{}".format(start, end)
yield e
def test(self):
params = dict()
results = list()
for i in range(100):
res = next(self.analyse_entry(Entry(nif__isString="Hello"),
params))
res.validate()
results.append(res.sentiments[0]['marl:hasPolarity'])
assert 'marl:Positive' in results
assert 'marl:Negative' in results
def analyse(self, *args, **kwargs):
logger.warn('Analysing with the example.')
logger.warn('The answer to this response is: %s.' %
kwargs['parameter'])
resp = Response()
ent = Entry(kwargs['input'])
ent['example:reversed'] = kwargs['input'][::-1]
ent['example:the_answer'] = kwargs['parameter']
resp.entries.append(ent)
return resp
def analyse(self, **params):
lang = params.get("language", "auto")
response = Results()
polarity_value = max(-1, min(1, random.gauss(0.2, 0.2)))
polarity = "marl:Neutral"
if polarity_value > 0:
polarity = "marl:Positive"
elif polarity_value < 0:
polarity = "marl:Negative"
entry = Entry({"id":":Entry0",
"nif:isString": params["input"]})
sentiment = Sentiment({"id": ":Sentiment0",
"marl:hasPolarity": polarity,
"marl:polarityValue": polarity_value})
sentiment["prov:wasGeneratedBy"] = self.id
entry.sentiments = []
entry.sentiments.append(sentiment)
entry.language = lang
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, **params):
lang = params.get("language", "auto")
response = Results()
polarity_value = max(-1, min(1, random.gauss(0.2, 0.2)))
polarity = "marl:Neutral"
if polarity_value > 0:
polarity = "marl:Positive"
elif polarity_value < 0:
polarity = "marl:Negative"
entry = Entry({"id": ":Entry0", "nif:isString": params["input"]})
sentiment = Sentiment({
"id": ":Sentiment0",
"marl:hasPolarity": polarity,
"marl:polarityValue": polarity_value
})
sentiment["prov:wasGeneratedBy"] = self.id
entry.sentiments = []
entry.sentiments.append(sentiment)
entry.language = lang
response.entries.append(entry)
return response
def test_response(self):
r = Response(context=os.path.normpath(
os.path.join(__file__, "..", "..", "context.jsonld")))
assert("@context" in r)
assert(r._frame)
logging.debug("Default frame: %s", r._frame)
assert("marl" in r.context)
assert("entries" in r.context)
r2 = Response(context=json.loads('{"test": "roger"}'))
assert("test" in r2.context)
r3 = Response(context=None)
del r3.context
assert("@context" not in r3)
assert("entries" in r3)
assert("analysis" in r3)
r4 = Response()
assert("@context" in r4)
assert("entries" in r4)
assert("analysis" in r4)
dummy = SenpyPlugin({"name": "dummy", "version": 0})
r5 = Response({"dummy": dummy}, context=None, frame=None)
logging.debug("Response 5: %s", r5)
assert("dummy" in r5)
assert(r5["dummy"].name == "dummy")
js = r5.jsonld(context={}, frame={})
logging.debug("jsonld 5: %s", js)
assert("dummy" in js)
assert(js["dummy"].name == "dummy")
r6 = Response()
r6.entries.append(Entry(text="Just testing"))
logging.debug("Reponse 6: %s", r6)
assert("@context" in r6)
assert("marl" in r6.context)
assert("entries" in r6.context)
js = r6.jsonld()
logging.debug("jsonld: %s", js)
assert("entries" in js)
assert("entries" in js)
assert("analysis" in js)
resp = r6.flask()
received = json.loads(resp.data)
logging.debug("Response: %s", js)
assert(received["entries"])
assert(received["entries"][0]["text"] == "Just testing")
assert(received["entries"][0]["text"] != "Not testing")
def analyse(self, **params):
lang = params.get("language", "auto")
p = params.get("prefix", None)
response = Response(prefix=p)
polarity_value = max(-1, min(1, random.gauss(0.2, 0.2)))
polarity = "marl:Neutral"
if polarity_value > 0:
polarity = "marl:Positive"
elif polarity_value < 0:
polarity = "marl:Negative"
entry = Entry(id="Entry0",
text=params["input"],
prefix=p)
opinion = Opinion(id="Opinion0",
prefix=p,
hasPolarity=polarity,
polarityValue=polarity_value)
opinion["prov:wasGeneratedBy"] = self.id
entry.opinions.append(opinion)
entry.language = lang
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):
classifier = params.get("classifier", "polyglot-es")
p = params.get("prefix", None)
response = Results(prefix=p)
(entities, types, startIndexes,
endIndexes) = wrapper.service(params.get("input"), classifier)
print(entities, types, startIndexes, endIndexes)
for x in range(0, len(entities)):
entry = Entry(id="Entry" + str(x),
prefix=p,
anchorOf=entities[x],
taClassRef="dbo:" + types[x],
startIndex=startIndexes[x],
endIndex=endIndexes[x])
response.entries.append(entry)
return response
def test_convert_emotions(self):
self.senpy.activate_all(sync=True)
plugin = Plugin({
'id': 'imaginary',
'onyx:usesEmotionModel': 'emoml:fsre-dimensions'
})
eSet1 = EmotionSet()
activity = plugin.activity()
eSet1.prov(activity)
eSet1['onyx:hasEmotion'].append(
Emotion({
'emoml:arousal': 1,
'emoml:potency': 0,
'emoml:valence': 0
}))
response = Results({
'activities': [activity],
'entries': [
Entry({
'nif:isString': 'much ado about nothing',
'onyx:hasEmotionSet': [eSet1]
})
]
})
params = {
'emotion-model': 'emoml:big6',
'algorithm': ['conversion'],
'conversion': 'full'
}
r1 = deepcopy(response)
r1.parameters = params
self.senpy.analyse(r1)
assert len(r1.entries[0].emotions) == 2
params['conversion'] = 'nested'
r2 = deepcopy(response)
r2.parameters = params
self.senpy.analyse(r2)
assert len(r2.entries[0].emotions) == 1
assert r2.entries[0].emotions[0]['prov:wasDerivedFrom'] == eSet1
params['conversion'] = 'filtered'
r3 = deepcopy(response)
r3.parameters = params
self.senpy.analyse(r3)
assert len(r3.entries[0].emotions) == 1
r3.jsonld()
def test_convert_emotions(self):
self.senpy.activate_all(sync=True)
plugin = Plugin({
'id': 'imaginary',
'onyx:usesEmotionModel': 'emoml:fsre-dimensions'
})
eSet1 = EmotionSet()
eSet1.prov__wasGeneratedBy = plugin['id']
eSet1['onyx:hasEmotion'].append(
Emotion({
'emoml:arousal': 1,
'emoml:potency': 0,
'emoml:valence': 0
}))
response = Results({
'entries':
[Entry({
'text': 'much ado about nothing',
'emotions': [eSet1]
})]
})
params = {'emotionModel': 'emoml:big6', 'conversion': 'full'}
r1 = deepcopy(response)
self.senpy.convert_emotions(r1, [
plugin,
], params)
assert len(r1.entries[0].emotions) == 2
params['conversion'] = 'nested'
r2 = deepcopy(response)
self.senpy.convert_emotions(r2, [
plugin,
], params)
assert len(r2.entries[0].emotions) == 1
assert r2.entries[0].emotions[0]['prov:wasDerivedFrom'] == eSet1
params['conversion'] = 'filtered'
r3 = deepcopy(response)
self.senpy.convert_emotions(r3, [
plugin,
], params)
assert len(r3.entries[0].emotions) == 1
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("Analysing with params {}".format(params))
text = params.get("input", None)
tokens = self._tokenize(text)
tokens = self._pos(tokens)
for i in tokens:
tokens[i]['lemmas'] = {}
for w in tokens[i]['tokens']:
lemmas = wn.lemmas(w[0], lang='spa')
if len(lemmas) == 0:
continue
tokens[i]['lemmas'][w[0]] = lemmas
logger.debug("Tokens: {}".format(tokens))
trans = TextBlob(unicode(text)).translate(from_lang='es',to='en')
useful_synsets = {}
for s_i, t_s in enumerate(trans.sentences):
useful_synsets[s_i] = {}
for w_i, t_w in enumerate(trans.sentences[s_i].words):
synsets = wn.synsets(trans.sentences[s_i].words[w_i])
if len(synsets) == 0:
continue
eq_synset = self._compare_synsets(synsets, tokens, s_i)
useful_synsets[s_i][t_w] = eq_synset
logger.debug("Synsets used for analysis: {}".format(useful_synsets))
scores = {}
for i in tokens:
scores[i] = {}
if useful_synsets is None:
for word in useful_synsets[i]:
if useful_synsets[i][word] is None:
continue
temp_scores = self._swn.get_score(useful_synsets[i][word].name().split('.')[0].replace(' ',' '))
for score in temp_scores:
if score['synset'] == useful_synsets[i][word]:
t_score = score['pos'] - score['neg']
f_score = 'neu'
if t_score > 0:
f_score = 'pos'
elif t_score < 0:
f_score = 'neg'
score['score'] = f_score
scores[i][word] = score
break
logger.debug("All scores (some not used): {}".format(scores))
lang = params.get("language", "auto")
p = params.get("prefix", None)
response = Results()
for i in scores:
n_pos = 0.0
n_neg = 0.0
for w in scores[i]:
if scores[i][w]['score'] == 'pos':
n_pos += 1.0
elif scores[i][w]['score'] == 'neg':
n_neg += 1.0
inter = interp1d([-1.0, 1.0], [0.0, 1.0])
try:
g_score = (n_pos - n_neg) / (n_pos + n_neg)
g_score = float(inter(g_score))
except:
if n_pos == 0 and n_neg == 0:
g_score = 0.5
polarity = 'marl:Neutral'
if g_score > 0.5:
polarity = 'marl:Positive'
elif g_score < 0.5:
polarity = 'marl:Negative'
entry = Entry(id="Entry"+str(i),
nif_isString=tokens[i]['sentence'])
opinion = Sentiment(id="Opinion0"+'_'+str(i),
marl__hasPolarity=polarity,
marL__polarityValue=float("{0:.2f}".format(g_score)))
opinion["prov:wasGeneratedBy"] = self.id
entry.sentiments = []
entry.sentiments.append(opinion)
entry.language = lang
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
