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, **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, **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, **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, **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 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 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 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 = 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
