def doc_sentiment(data):
"""
"""
#Call SenticNet module
sn = SenticNet()
#Create positive and negative variables
total_sentiment = 0
#Calculate sentiment for all words in document
for i in range(len(data)):
#If words don't exist in SenticNet vocabulary they will return an error
#We treat these words as if they have a sentiment of 0
try:
#Calculate sentiment of word
sentiment = sn.polarity_value(data[i])
#Update total sentiment
total_sentiment += float(sentiment)
except:
None
try:
#If total sentiment = 0 division errors will occur
#Calculate average sentiment for the document
avg_sentiment = total_sentiment/len(data)
except:
avg_sentiment = 0
if avg_sentiment >= 0:
output = 1
else:
output = 0
return output
def Terms_Chooser(data, n_of_words, polarity_threshold):
sn = SenticNet()
#choosing words
data["Content"] = data["Content"].apply(lambda row: nltk.word_tokenize(row))
lista = np.array(data["Content"].values.tolist())
lista = list(itertools.chain.from_iterable(lista))
FD = FreqDist(lista)
MC = FD.most_common(n_of_words)
common_words = []
for i in range (0,n_of_words):
common_words.append(MC[i][0])
polarity = list()
words = list()
for x in common_words:
try:
temp = sn.polarity_intense(x)
if (float(temp) > polarity_threshold or float(temp) < -(polarity_threshold)):
polarity.append(temp)
words.append(x)
except Exception:
continue
return words
class Dictionary:
def __init__(self):
self.sn = SenticNet()
"""
Input : String
Output : "positive" or "negative"
"""
def get_word_polarity(self, word, log=True):
value = "empty"
try:
value = self.sn.polarity_value(word.lower())
except:
if log:
print('An error occurred. Word: ' + word + ' is not known.')
return value
"""
Input : String
Output : Int [-1 : 1]
"""
def get_word_polarity_numerical_value(self, word, log=True):
value = "empty"
try:
value = self.sn.polarity_intense(word.lower())
except:
if log:
print('An error occurred. Word: ' + word + ' is not known.')
return value
def senticnet(text):
"""
Returns a list obtained from SenticNet with the following four features normalized: [pleasantness_value, attention_value, sensiivity_value, aptitude_value]
:param text: input text pre-processed by Spacy
:return: a list with the SenticNet features averaged for all the words in text
"""
list_features = [0] * 4
sn = SenticNet()
count_words = 0
for token in text:
try:
concept_info = sn.concept(token)
list_features[0] += float(concept_info['sentics']['pleasantness'])
list_features[1] += float(concept_info['sentics']['attention'])
list_features[2] += float(concept_info['sentics']['sensitivity'])
list_features[3] += float(concept_info['sentics']['aptitude'])
count_words += 1
except KeyError:
pass
if count_words != 0:
list_features = [feature / count_words for feature in list_features]
return list_features
def getSentics(self, word):
senticsAndItensity = []
sn = SenticNet('en')
try:
sentics = sn.sentics(word)
polarity_intensity = sn.polarity_intense(word)
# print(sentics)
# print(sentics['pleasantness'])
# print(sentics['attention'])
# print(sentics['sensitivity'])
# print(sentics['aptitude'])
# print(polarity_intensity)
senticsAndItensity.append(float(sentics['pleasantness']))
senticsAndItensity.append(float(sentics['attention']))
senticsAndItensity.append(float(sentics['sensitivity']))
senticsAndItensity.append(float(sentics['aptitude']))
senticsAndItensity.append(float(polarity_intensity))
return senticsAndItensity
except Exception as e:
defaultsentics = [0.0, 0.0, 0.0, 0.0, 0.0]
return defaultsentics
# ##TESTING AREA
# yas = SenticValuer()
# print(yas.getSentics("awkward"))
def sem(d):
try:
sn = SenticNet()
sn.semantics(d)
return True
except KeyError:
return False
def fun1(d):
try:
from senticnet.senticnet import SenticNet
sn = SenticNet()
sn.semantics(d)
return True
except KeyError as error:
return False
def get_emotions(tokens):
from senticnet.senticnet import SenticNet
result = {}
sn = SenticNet()
for token in tokens:
moodtags = ""
if token in sn.data:
moodtags = sn.moodtags(token)
print(token, moodtags)
#TODO
return result
def __init__(self):
print("Start SenticNet - Sentiment Analysis")
self.sp = Support()
self.sn = SenticNet()
self.corpus = self.sp.import_corpus_bank()
self.terminology = self.sp.import_bank_terminology(filename='bank_terminology')
self.data, self.label = self.sp.process_data(filename='bank_message',
size_msg=3,
clean=True,
replace_text=True,
stemmed=None,
lemmatize=None,
spelling=None)
def getMaxSum_senti(text):
wnl = WordNetLemmatizer()
sn = SenticNet()
sentences = nltk.sent_tokenize(text)
text_sentiAvg = 0
sentence_maxSenti = 0
for index in range(len(sentences)):
sentence = sentences[index].strip()
sentence = sentence[0:-1]
assert '.' not in sentence
words = nltk.word_tokenize(sentence.lower())
pos_tags = nltk.pos_tag(words)
sentence_sentiSum = getSentenceSentiSum(pos_tags, wnl, sn)
# print sentence_sentiSum,
if sentence_sentiSum > sentence_maxSenti:
sentence_maxSenti = sentence_sentiSum
text_sentiAvg += sentence_sentiSum
text_sentiAvg = text_sentiAvg / len(sentences)
text_sentiAvg = round(text_sentiAvg, 6)
sentence_maxSenti = round(sentence_maxSenti, 6)
return text_sentiAvg, sentence_maxSenti
class SenticNets(BaseEstimator, TransformerMixin):
def __init__(self, vocab):
self.vocab = vocab
self.X_width = len(vocab)
self.sn = SenticNet()
def fit(self, X):
return self
def vector(self, X):
X = X.split(' ')
zeros = csr_matrix((1, self.X_width))
for word in range(len(X)):
if not X[word] in self.vocab:
continue
try:
score = self.sn.polarity_value(X[word])
except KeyError:
continue
zeros[0, self.vocab[X[word]]] = score
return zeros
def transform(self, X):
self.zeros = csr_matrix((0, self.X_width))
self.X_length = X.shape[0]
for i in range(self.X_length):
self.zeros = vstack([self.zeros, self.vector(X[i])])
return self.zeros
def fit_transform(self, X, y=None):
self.fit(X)
self.transform(X)
return self.zeros
def __init__(self, *args, **kwargs):
# loading necessaries
self.nlp = spacy.load("en_core_web_sm")
self.senti = SenticNet()
self.sid = SentimentIntensityAnalyzer()
#loading dataset
self.df = pd.read_json("./Sarcasm_Headlines_Dataset.json", lines=True)
self.df = self.df[:15000]
self.df.drop(columns="article_link",
inplace=True) #dropping unnessary attribute
#storing nlp data in headlines variable
self.headlines = []
self.uni_gram = set()
self.uni_feature = []
self.y_ = []
for i in self.df['headline']:
self.headlines.append(self.nlp(i))
def sentiment_avg(self, text):
sn = SenticNet('pt')
list_polarity = []
qtd_words = len(text)
temp = text.split()
avg_n = 0
for i in range(len(temp)):
try:
polarity_value = sn.polarity_value(
self.treatment_string(temp[i]))
list_polarity.append(polarity_value)
except:
qtd_words -= 1
i += 1
avg_n = self.avg(list_polarity, qtd_words)
if avg_n > 0.003 or avg_n < -0.003:
return True
else:
return False
class SenticNetSA:
def __init__(self):
print("Start SenticNet - Sentiment Analysis")
self.sp = Support()
self.sn = SenticNet()
self.corpus = self.sp.import_corpus_bank()
self.terminology = self.sp.import_bank_terminology(filename='bank_terminology')
self.data, self.label = self.sp.process_data(filename='bank_message',
size_msg=3,
clean=True,
replace_text=True,
stemmed=None,
lemmatize=None,
spelling=None)
def baseline(self):
TP = 0
FP = 0
FN = 0
x_train, x_test, y_train, y_test = train_test_split(self.data, self.label, test_size=0.20, random_state=1000)
for i in range (0, len(x_train)):
msg = str(x_train[i])
value = float(y_train[i])
result = self.sn.message_concept(msg)
polarity_value = float(result['polarity_value'])
polarity_value = 0.0 if polarity_value < 0.10 or polarity_value > -0.1 else polarity_value
if value == polarity_value:
TP += 1
else:
FP += 1
if value == 1 and (polarity_value == 0.0 or polarity_value == -1.0):
FN += 1
elif value == 0.0 and (polarity_value == 1 or polarity_value == -1.0):
FN += 1
elif value == -1.0 and (polarity_value == 0.0 or polarity_value == 1.0):
FN += 1
precision = TP/(TP + FP)
recall = TP / (TP + FN)
f1 = 2*((precision*recall) / (precision + recall))
print("f1-score : {}%".format(round(f1 * 100, 2)))
# untuk mencari frekuensi kemunculan kata (menggunakan list asli, bukan list kata unik)
from nltk.probability import FreqDist
fdist = FreqDist(ALL_filtered_sentence)
print("fdist : {}".format(fdist))
print(fdist.most_common(200))
# ------------------------------------------------------------SENTICNET
from MdCek.DBRepository.WordList_sentic_Repository import WordList_sentic_Repository
from MdCek.Model.WordList_sentic import WordList_sentic
import pprint
from senticnet.senticnet import SenticNet
wordSentic = WordList_sentic_Repository()
sn = SenticNet()
print("-----------------------------------------membaca wordlist depresion yang sudah ada dari NLTK dan textblob")
# EXISTING WORDLIST NEGATIVE
existWordlistDepression = wordList_depRepo.read()
lemmaOfExistWordlist = []
conceptExist = []
conceptExistNegative = []
objsConceptExistNegative = []
conceptExistPositive = []
objsConceptExistPositive = []
sisa = 0
# print("Jumlah existing wordlist : {} ".format(len(existWordlistDepression)))
document_term_matrix_idf.head(10)
# word cloud
# wordcloud using TFIDF of words, some of the docs some words keep repeating in all the docs, (Science) It works as log functions to normalize the words
words = dict(document_term_matrix_idf.apply(
sum, axis=0)) ## this needs an dictionary object
wordcloud = WordCloud(
max_font_size=40, max_words=50, background_color="white").fit_words(
words) # fit_words() is used to plot wordcloud using dictionary.
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
##### positive and negative words using user-built lexicons, plotting their wordclouds.
sn = SenticNet()
positive_words = []
negative_words = []
for word in vectorizer.get_feature_names():
if word in sn.data:
if sn.polarity_value(word) == 'positive':
positive_words.append(word)
if sn.polarity_value(word) == 'negative':
negative_words.append(word)
len(positive_words)
len(negative_words)
positive_words = dict(document_term_matrix[positive_words].apply(sum, axis=0))
negative_words = dict(document_term_matrix[negative_words].apply(sum, axis=0))
for _, records in groupby(sorted(lst, key=keyprop), keyprop)
]
a = [{'time': '25 APR', 'total': 10, 'high': 10},
{'time': '26 APR', 'total': 5, 'high': 5}]
b = [{'time': '24 APR', 'total': 10, 'high': 10},
{'time': '26 APR', 'total': 15, 'high': 5}]
merger = merge_list_of_records_by('time', add)
hasil_merge = merger(a+b)
print(hasil_merge)
print("sinonim with thesaurus==================================================================")
# from PyDictionary import PyDictionary
#
# dictionary = PyDictionary()
# print(dictionary.synonym("good"))
from thesaurus import Word
w = Word('suicidal')
syn = w.synonyms()
print(syn)
sn = SenticNet()
try:
concept_info_sinonim = sn.concept("suicidal")
print(concept_info_sinonim)
except Exception as e:
print(e)
def get_clues(text):
text = text
print("*--------(%s)-------------*" % (text))
print(type(text))
nlp = StanfordCoreNLP('http://localhost:9001')
stop_words = set(stopwords.words('english'))
'''
Method to remove numbers appended at last
'''
dep_parse = nlp.annotate(text,
properties={
'annotators': 'depparse',
'outputFormat': 'json',
'timeout': 10000,
})
pos = nlp.annotate(text,
properties={
'annotators': 'lemma',
'outputFormat': 'json',
'timeout': 10000,
})
sn = SenticNet()
word_to_dep = [{} for i in range(len(dep_parse['sentences']))]
word_to_par = [{} for i in range(len(dep_parse['sentences']))]
word_to_pos = [{} for i in range(len(dep_parse['sentences']))]
word_to_lemma = [{} for i in range(len(dep_parse['sentences']))]
word_to_child = [{} for i in range(len(dep_parse['sentences']))]
sents = [[] for i in range(len(dep_parse['sentences']))]
index_to_word = {}
'''
Constructing dicts for maintaining the dependencies among words.
'''
'''
Appending each word by occurence number to maintain distinct word count
'''
#print(dep_parse['sentences'])
print("********")
for i, sent in enumerate(dep_parse['sentences']):
for dep in sent['basicDependencies']:
word_to_dep[i][dep['dependentGloss'] +
str(dep['dependent'])] = dep['dep']
word_to_par[i][dep['dependentGloss'] +
str(dep['dependent'])] = dep['governorGloss'] + str(
dep['governor'])
index_to_word[dep['dependentGloss'] +
str(dep['dependent'])] = dep['dependentGloss']
if (dep['governorGloss'] + str(dep['governor'])
not in word_to_child[i]):
word_to_child[i][dep['governorGloss'] +
str(dep['governor'])] = []
if (dep['dependentGloss'] + str(dep['dependent'])
not in word_to_child[i]):
word_to_child[i][dep['dependentGloss'] +
str(dep['dependent'])] = []
word_to_child[i][dep['governorGloss'] +
str(dep['governor'])].append(
dep['dependentGloss'] + str(dep['dependent']))
sents[i].append(dep['dependentGloss'] + str(dep['dependent']))
word_to_dep[i]['ROOT0'] = 'root'
word_to_par[i]['ROOT0'] = 'root'
for i, sent in enumerate(pos['sentences']):
for pos_tagger in sent['tokens']:
word_to_pos[i][pos_tagger['word']] = pos_tagger['pos']
word_to_lemma[i][pos_tagger['word']] = pos_tagger['lemma']
word_to_pos[i]['ROOT'] = 'root'
word_to_lemma[i]['ROOT'] = 'root'
'''
Displaying the deps
'''
##Implemeting rules to extract aspects
for i, sent in enumerate(sents):
if (__name__ == '__main__'):
print(word_to_dep[i], word_to_par[i], word_to_pos[i])
print("Children==>")
print(word_to_child[i])
aspects = []
for i, sent in enumerate(sents):
for word in sent:
'''
Rule 0
'''
if ('subj' in word_to_dep[i][word]):
for child in word_to_child[i][word_to_par[i][word]]:
if ('amod' in word_to_dep[i][child]
or 'advmod' in word_to_dep[i][child]):
aspects.append(word_to_par[i][word])
if (__name__ == '__main__'):
print("Rule 0 triggered.")
'''
Rule 1 (without sub): Very big to hold.
'''
if (word_to_dep[i][word] == 'xcomp' and
('JJ' in word_to_pos[i][index_to_word[word_to_par[i][word]]] or
'RB' in word_to_pos[i][index_to_word[word_to_par[i][word]]])):
if (__name__ == '__main__'):
print("Rule 1 triggered")
aspects.append(word_to_par[i][word])
'''
Rule 2 (without subj): Not to mention the price of the phone
'''
if (word_to_dep[i][word] == 'dobj' and 'VB'
in word_to_pos[i][index_to_word[(word_to_par[i][word])]]
and ('NN' in word_to_pos[i][index_to_word[(word)]]
or 'JJ' in word_to_pos[i][index_to_word[(word)]])):
aspects.append(word)
if (__name__ == '__main__'):
print("Rule 2 triggered")
print(word)
'''
Rule 3 (without subj): Love the sleekness of the player
'''
if ('NN' in word_to_pos[i][index_to_word[(word)]]
and word_to_dep[i][word] == 'nmod'):
aspects.append(word_to_par[i][word])
if (__name__ == '__main__'):
print("Rule 3 triggered")
print(word_to_par[i][word])
'''
Rule 4 (with sub): The battery lasts little
two aspects
'''
if (word_to_dep[i][word] == 'advmod'
or word_to_dep[i][word] == 'amod' or word_to_dep[i][word]
== 'advcl') and ('VB' in word_to_pos[i][index_to_word[(
word_to_par[i][word])]]):
aspects.append(word_to_par[i][word])
for word2 in sent:
if (word2 != word and word_to_dep[i][word2] == 'nsubj'
and word_to_par[i][word2] == word_to_par[i][word]
and
('NN' in word_to_pos[i][index_to_word[word2]]
or 'JJ' in word_to_pos[i][index_to_word[word2]])):
aspects.append(word2)
if (__name__ == '__main__'):
print("Rule 4 triggered")
print(word2)
'''
Rule 5 (with sub): I like the lens of this camera
'''
if ('NN' in word_to_pos[i][index_to_word[(word)]]
and word_to_dep[i][word] == 'dobj'):
if (__name__ == '__main__'):
print("Rule 5 triggered")
print(word)
try:
concept_info = sn.concept((word))
print("present in senticnet")
except KeyError:
print("Yay")
aspects.append(word)
'''
Rule 6 : I like the beauty of the screen.
Check if senticnet condition should be added
'''
if ('NN' in word_to_pos[i][index_to_word[(word)]]
and word_to_dep[i][word] == 'dobj'):
try:
concept_info = sn.concept((word))
aspects.append(word)
print("yay!")
except KeyError:
print("oops, not there in SenticNet")
for word2 in sent:
if (word2 != word and word_to_par[i][word2] == word and
'NN' in word_to_pos[i][index_to_word[(word2)]]):
aspects.append(word2)
if (__name__ == '__main__'):
print("Rule 6 triggered.")
print(word2)
'''
Rule 7 : I would like to comment on the camera of this phone.
'''
if (word_to_dep[i][word] == 'xcomp'):
try:
concept_info = sn.concept((word))
aspects.append(word)
print("yay!")
except KeyError:
print("oops, not there in SenticNet")
for child in word_to_child[i][word]:
if ('NN' in word_to_pos[i][index_to_word[child]]):
aspects.append(child)
if (__name__ == '__main__'):
print("Rule 7 triggered.")
print(word)
print(child)
'''
Rule 8 : The car is expensive.
'''
if (word_to_dep[i][word] == 'nsubj'):
for word2 in sent:
if (word2 != word and word_to_dep[i][word2] == 'cop'
and word_to_par[i][word2] == word_to_par[i][word]):
aspects.append(word_to_par[i][word])
if (__name__ == '__main__'):
print("Rule 8 triggered")
print(word_to_par[i][word])
'''
Rule 9 : The camera is nice.
'''
if (word_to_dep[i][word] == 'nsubj'
and 'NN' in word_to_pos[i][index_to_word[(word)]]):
for word2 in sent:
if (word2 != word and word_to_dep[i][word2] == 'cop'
and word_to_par[i][word2] == word_to_par[i][word]):
aspects.append(word)
if (__name__ == '__main__'):
print("Rule 9 triggered")
print(word)
'''
Rule 10 : The phone is very lightweight to carry.
'''
if (word_to_dep[i][word] == 'cop'):
for word2 in sent:
if (word2 != word
and 'VB' in word_to_pos[i][index_to_word[(word2)]]
and word_to_par[i][word] == word_to_par[i][word2]):
aspects.append(word2)
if (__name__ == '__main__'):
print("Rule 10 triggered.")
print(word2)
'''
Extracting mods of dobjs
'''
if (word_to_dep[i][word] == 'dobj'):
for child in word_to_child[i][word]:
if ('mod' in word_to_dep[i][child] and 'JJ'
in word_to_pos[i][index_to_word[(child)]]):
aspects.append(child)
'''
Rule 11 : Checking for conjuctions
'''
for asp in aspects:
for word in sent:
if (word_to_dep[i][word] == 'conj'
and word_to_par[i][word] == asp):
aspects.append(word)
if (__name__ == '__main__'):
print("Rule conj triggered.")
print(word)
finalIAC = set(aspects)
finalIAC = [index_to_word[f] for f in finalIAC]
finalIAC = [w for w in finalIAC if not w in stop_words]
finalSenti = []
for iac in finalIAC:
try:
concept_info = sn.concept((iac))
finalSenti.append(iac)
except KeyError:
print("No word available for " + iac)
return finalIAC, finalSenti
class Get_IAC():
def __init__(self):
self.col = ['Name', 'Brand', 'Price', 'Title', 'Score', 'Time', 'Text']
self.sn = SenticNet('en')
self.wordnet_lemmatizer = WordNetLemmatizer()
def review_to_sentences(self, review):
# review = review.replace(',','.')
review = review.replace('.', '. ')
raw_sentences = sent_tokenize(review)
return raw_sentences
def InputData(self, input_path):
self.dict_list = []
if '.csv' in input_path:
with open(input_path, 'r', encoding='utf8') as f:
reader = csv.DictReader(f)
for row in reader:
d = {i: row[i] for i in col}
self.dict_list.append(d)
elif '.xlsx' in input_path:
wb = load_workbook(input_path)
sheet = wb.active
count = 0
for row in sheet.rows:
if count == 0:
count += 1
continue
d = {}
name = 0
for cell in row:
d[self.col[name]] = cell.value
name += 1
self.dict_list.append(d)
self.dict_list = [
x for x in self.dict_list if x['Text'] != '' and x['Text'] != None
]
self.sentences = []
for i in range(len(self.dict_list)):
for j in self.review_to_sentences(self.dict_list[i]['Text']):
self.sentences.append(j)
self.sentences = [x for x in self.sentences if len(x) >= 5]
def GetIAC(self):
self.nlp = StanfordCoreNLP(r'stanford-corenlp-full-2018-10-05')
self.IAC = []
for i in tqdm(self.sentences):
dependency = self.nlp.dependency_parse(i)
token = self.nlp.word_tokenize(i)
if [x for x in dependency if 'compound' in x] != []:
for j in [x for x in dependency if 'compound' in x]:
token[j[2] - 1] = token[j[2] - 1] + '-' + token[j[1] - 1]
token[j[1] - 1] = ''
i = ' '.join(token)
parse = self.nlp.parse(i)
dependency = self.nlp.dependency_parse(i)
pos = self.nlp.pos_tag(i)
token = []
for j in pos:
wordnet_pos = self.get_wordnet_pos(j[1])
token.append(
self.wordnet_lemmatizer.lemmatize(j[0].lower(),
pos=wordnet_pos))
# subject noun relation
if [x for x in dependency if 'nsubj' in x] != []:
for j in self.Subject_Noun_Rule(parse, dependency, token, pos):
self.IAC.append(j)
else: # Non subject noun relation
for j in self.Non_Subject_Noun_Rule(parse, dependency, token,
pos):
self.IAC.append(j)
self.nlp.close()
self.IAC = list(set(self.IAC))
def get_wordnet_pos(self, treebank_tag):
if treebank_tag.startswith('J'):
return wn.ADJ
elif treebank_tag.startswith('V'):
return wn.VERB
elif treebank_tag.startswith('N'):
return wn.NOUN
elif treebank_tag.startswith('R'):
return wn.ADV
else:
return wn.NOUN
# Additional Rule: 對等連接詞
def Conj(self, index, dependency, token):
IAC = []
index = list(set(index))
if [x for x in dependency if 'conj' in x] != []:
conj = [x for x in dependency if 'conj' in x]
for j in conj:
if j[1] in index or j[2] in index:
if j[1] not in index:
IAC.append(token[j[1] - 1])
index.append(j[1])
if j[2] not in index:
IAC.append(token[j[2] - 1])
index.append(j[2])
return IAC
def Subject_Noun_Rule(self, parse, dependency, token, pos):
be = ['is', 'was', 'am', 'are', 'were']
adv_mod = [x for x in dependency if 'advmod' in x]
adj_mod = [x for x in dependency if 'amod' in x]
active_token = token[[x for x in dependency if 'nsubj' in x][0][2] -
1] # 主詞
result = []
index = []
if adv_mod != [] or adj_mod != []:
A, B = self.Rule1(adv_mod, adj_mod, active_token, token)
result += A
index += B
# does not have auxiliary verb
if any(k in token
for k in be) == False and [x for x in pos if 'MD' in x] == []:
A, B = self.Rule2(token, pos, dependency, active_token, adv_mod,
adj_mod)
result += A
index += B
if [x for x in dependency if 'dobj' in x] != []:
A, B = self.Rule3(dependency, token, pos)
result += A
index += B
if [x for x in dependency if 'xcomp' in x] != []:
A, B = self.Rule4(dependency, token, pos)
result += A
index += B
if [x for x in dependency if 'cop' in x] != []:
A, B = self.Rule5(dependency, pos, active_token, token)
result += A
index += B
result += self.Conj(index, dependency, token)
return list(set(result))
# 3.3.3 Rule 1
def Rule1(self, adv_mod, adj_mod, active_token, token):
IAC = []
index = []
if adv_mod != []:
for j in adv_mod:
try:
concept = self.sn.concept(token[j[2] - 1])
IAC.append(token[j[2] - 1])
index.append(j[2])
except:
a = 0
# print(token[j[2]-1] + ' Not in SenticNet')
if adj_mod != []:
for j in adj_mod:
try:
concept = self.sn.concept(token[j[2] - 1])
IAC.append(token[j[2] - 1])
index.append(j[2])
except:
a = 0
# print(token[j[2]-1] + ' Not in SenticNet')
return IAC, index
# 3.3.3 Rule 2-1
def Rule2(self, token, pos, dependency, active_token, adv_mod, adj_mod):
IAC = []
index = []
advcl = [x for x in dependency
if 'advcl' in x] # adverbial clause modifier
if advcl != []:
for j in advcl:
IAC.append(token[j[1] - 1])
index.append(j[1])
IAC.append(active_token)
index.append([x for x in dependency if 'nsubj' in x][0][2])
if adv_mod != []:
for j in adv_mod:
IAC.append(token[j[1] - 1])
index.append(j[1])
IAC.append(active_token)
index.append([x for x in dependency if 'nsubj' in x][0][2])
if adj_mod != []:
for j in adj_mod:
IAC.append(token[j[1] - 1])
index.append(j[1])
IAC.append(active_token)
index.append([x for x in dependency if 'nsubj' in x][0][2])
return IAC, index
# 3.3.3 Rule 2-2 & 2-3
def Rule3(self, dependency, token, pos):
IAC = []
index = []
dobj = [x for x in dependency
if 'dobj' in x] # open clausal complement
for j in dobj:
if pos[j[2] - 1][1] == 'NN':
try:
# Rule 2-3
concept = self.sn.concept(token[j[2] - 1])
IAC.append(token[j[2] - 1])
index.append(j[2])
conj = []
conj.append(j[2])
if [x for x in dependency if 'conj' in x and j[2] in x
] != []:
for i in [
x for x in dependency
if 'conj' in x and j[2] in x
]:
conj.append(i[1])
conj.append(i[2])
conj = list(set(conj))
for i in conj:
t1 = i
connect = [x for x in dependency if t1 in x]
for k in connect:
if k[1] != t1:
if pos[k[1] - 1][1] == 'NN':
IAC.append(token[k[1] - 1])
index.append(k[1])
if k[2] != t1:
if pos[k[2] - 1][1] == 'NN':
IAC.append(token[k[2] - 1])
index.append(k[2])
except:
# Rule 2-2
IAC.append(token[j[2] - 1])
index.append(j[2])
# print(token[j[2]-1] + ' Not in SenticNet')
return IAC, index
# 3.3.3 Rule 2-4
def Rule4(self, dependency, token, pos):
IAC = []
index = []
xcomp = [x for x in dependency
if 'xcomp' in x] # open clausal complement
for j in xcomp:
try:
concept = self.sn.concept(token[j[1] - 1] + '-' +
token[j[2] - 1])
IAC.append(token[j[1] - 1] + '-' + token[j[2] - 1])
except:
a = 0
# print(token[j[1]-1] + '-' + token[j[2]-1] + ' Not in SenticNet')
t1 = j[2]
connect = [x for x in dependency if t1 in x]
for k in connect:
if pos[k[2] - 1][1] == 'NN':
IAC.append(token[k[2] - 1])
index.append(k[2])
return IAC, index
# 3.3.3 Rule 3 & 3.3.3 Rule 4 & 3.3.3 Rule 5
def Rule5(self, dependency, pos, active_token, token):
IAC = []
index = []
cop = [x for x in dependency if 'cop' in x] # copula
# Rule 4
if pos[[x for x in dependency if 'nsubj' in x][0][2] - 1][1] == 'NN':
IAC.append(active_token)
index.append([x for x in dependency if 'nsubj' in x][0][2])
# Rule 3 & Rule 5
for j in cop:
# Rule 3
conj = []
# if token[j[1]-1] in all_term:
IAC.append(token[j[1] - 1])
index.append(j[1])
conj.append(j[1])
if [x for x in dependency if 'conj' in x and j[1] in x] != []:
for i in [x for x in dependency if 'conj' in x and j[1] in x]:
conj.append(i[1])
conj.append(i[2])
# Rule 5
conj = list(set(conj))
for i in conj:
t1 = i
connect = [x for x in dependency if t1 in x]
for k in connect:
if k[1] != t1:
if pos[k[1] - 1][1] == 'VB' or pos[k[1] -
1][1] == 'VV':
IAC.append(token[k[1] - 1])
index.append(k[1])
if token[t1 - 1] not in IAC:
IAC.append(token[t1 - 1])
index.append(t1)
if k[2] != t1:
if pos[k[2] - 1][1] == 'VB' or pos[k[2] -
1][1] == 'VV':
IAC.append(token[k[2] - 1])
index.append(k[2])
if token[t1 - 1] not in IAC:
IAC.append(token[t1 - 1])
index.append(t1)
return IAC, index
def Non_Subject_Noun_Rule(self, parse, dependency, token, pos):
result = []
index = []
if [x for x in dependency if 'xcomp' in x] != []:
A, B = self.Rule6(dependency, token)
result += A
index += B
if [x for x in dependency if 'case' in x] != []:
A, B = self.Rule7(dependency, pos, token)
result += A
index += B
if [x for x in dependency if 'dobj' in x] != []:
A, B = self.Rule8(dependency, token)
result += A
index += B
result += self.Conj(index, dependency, token)
return list(set(result))
# 3.3.4 Rule 1
def Rule6(self, dependency, token):
IAC = []
index = []
xcomp = [x for x in dependency
if 'xcomp' in x] # open clausal complement
for j in xcomp:
# if token[j[1]-1] in all_term:
IAC.append(token[j[1] - 1])
index.append(j[1])
return IAC, index
# 3.3.4 Rule 2
def Rule7(self, dependency, pos, token):
IAC = []
index = []
case = [x for x in dependency
if 'case' in x] # a prepositional relation
for j in case:
if pos[j[1] - 1][1] == 'NN':
connect = [
x for x in dependency if j[1] in x and 'mod' in x[0]
]
for i in connect:
IAC.append(token[i[1] - 1])
IAC.append(token[i[2] - 1])
index.append(i[1])
index.append(i[2])
return list(set(IAC)), list(set(index))
# 3.3.4 Rule 3
def Rule8(self, dependency, token):
IAC = []
index = []
dobj = [x for x in dependency
if 'dobj' in x] # a direct object relation
for j in dobj:
IAC.append(token[j[2] - 1])
index.append(j[2])
return IAC, index
def Save(self, output_path):
with open(output_path, 'wb') as f:
pickle.dump(self.IAC, f)
def get_clues(text):
text = text
print("*--------(%s)-------------*" % (text))
print(type(text))
nlp = StanfordCoreNLP('http://localhost:9001')
stop_words = set(stopwords.words('english'))
'''
Method to remove numbers appended at last
'''
dep_parse = nlp.annotate(text,
properties={
'annotators': 'depparse',
'outputFormat': 'json',
'timeout': 10000,
})
pos = nlp.annotate(text,
properties={
'annotators': 'lemma',
'outputFormat': 'json',
'timeout': 10000,
})
sn = SenticNet()
word_to_dep = [{} for i in range(len(dep_parse['sentences']))]
word_to_par = [{} for i in range(len(dep_parse['sentences']))]
word_to_pos = [{} for i in range(len(dep_parse['sentences']))]
word_to_lemma = [{} for i in range(len(dep_parse['sentences']))]
word_to_child = [{} for i in range(len(dep_parse['sentences']))]
sents = [[] for i in range(len(dep_parse['sentences']))]
index_to_word = {}
aspect_result = [[] for i in range(len(dep_parse['sentences']))]
'''
Constructing dicts for maintaining the dependencies among words.
'''
'''
Appending each word by occurence number to maintain distinct word count
'''
print("********")
for i, sent in enumerate(dep_parse['sentences']):
for dep in sent['basicDependencies']:
word_to_dep[i][dep['dependentGloss'] +
str(dep['dependent'])] = dep['dep']
word_to_par[i][dep['dependentGloss'] +
str(dep['dependent'])] = dep['governorGloss'] + str(
dep['governor'])
index_to_word[dep['dependentGloss'] +
str(dep['dependent'])] = dep['dependentGloss']
if (dep['governorGloss'] + str(dep['governor'])
not in word_to_child[i]):
word_to_child[i][dep['governorGloss'] +
str(dep['governor'])] = []
if (dep['dependentGloss'] + str(dep['dependent'])
not in word_to_child[i]):
word_to_child[i][dep['dependentGloss'] +
str(dep['dependent'])] = []
word_to_child[i][dep['governorGloss'] +
str(dep['governor'])].append(
dep['dependentGloss'] + str(dep['dependent']))
sents[i].append(dep['dependentGloss'] + str(dep['dependent']))
word_to_dep[i]['ROOT0'] = 'root'
word_to_par[i]['ROOT0'] = 'root'
for i, sent in enumerate(pos['sentences']):
for pos_tagger in sent['tokens']:
word_to_pos[i][pos_tagger['word']] = pos_tagger['pos']
word_to_lemma[i][pos_tagger['word']] = pos_tagger['lemma']
word_to_pos[i]['ROOT'] = 'root'
word_to_lemma[i]['ROOT'] = 'root'
'''
Displaying the deps
'''
##Implemeting rules to extract aspects
for i, sent in enumerate(sents):
if (__name__ == '__main__'):
print(word_to_dep[i], word_to_par[i], word_to_pos[i],
word_to_lemma[i])
print("Children==>")
print(word_to_child[i])
for i, sent in enumerate(sents):
token_t = word_to_child[i]['ROOT0'][0]
is_sub = False
token_h = None
for child in word_to_child[i][token_t]:
if 'subj' in word_to_dep[i][child]:
is_sub = True
token_h = child
#If subject noun relationship present
if is_sub:
"""
Rule 0: if any adv or adj modifies the token t.
"""
for child in word_to_child[i][token_t]:
if ('amod' in word_to_dep[i][child]
or 'advmod' in word_to_dep[i][child]):
try:
concept_info = sn.concept(index_to_word[child])
aspect_result[i].append(token_t)
if __name__ == '__main__':
print("Rule 1 triggered.")
print("present in senticnet")
except KeyError:
print("OOps")
"""
Rule 1: The battery lasts little.
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'advmod' or word_to_dep[i][child]
== 'amod' or word_to_dep[i][child] == 'advcl') and (
'VB' in word_to_pos[i][index_to_word[token_t]]):
aspect_result[i].append(token_t)
aspect_result[i].append(token_h)
if __name__ == '__main__':
print("Rule 1 triggered.")
print(token_t)
print(token_h)
"""
Rule 2: I like the beauty of the screen (and I like the lens of this camera).
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'dobj'
and 'NN' in word_to_pos[i][index_to_word[child]]):
aspect_result[i].append(child)
if __name__ == '__main__':
print(child)
try:
concept_info = sn.concept(index_to_word[child])
if __name__ == '__main__':
print("Rule 2 triggered")
for grandchild in word_to_child[i][child]:
if ('NN' in word_to_pos[i][
index_to_word[grandchild]]):
aspect_result[i].append(grandchild)
print(grandchild)
except KeyError:
print("OOps")
"""
Rule 3: I would like to comment on the camera of this phone.
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'xcomp'):
try:
sn.concept(index_to_word[child])
aspect_result[i].append(child)
if __name__ == '__main__':
print(child)
except KeyError:
print("OOps")
for grandchild in word_to_child[i][child]:
if ('NN' in word_to_pos[i][index_to_word[grandchild]]):
aspect_result[i].append(grandchild)
if __name__ == '__main__':
print(grandchild)
print("Rule 3 triggered.")
"""
Rule 4: The car is expensive.
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'cop'):
try:
sn.concept(word_to_lemma[i][index_to_word[token_t]])
aspect_result[i].append(token_t)
if __name__ == '__main__':
print("Rule 4 triggered")
print(token_t)
except KeyError:
pass
"""
Rule 5: The camera is nice
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'cop'
and 'NN' in word_to_pos[i][index_to_word[token_h]]):
aspect_result[i].append(token_h)
if __name__ == '__main__':
print("Rule 5 triggered.")
print(token_h)
"""
Rule 6:
"""
for child in word_to_child[i][token_t]:
if (word_to_dep[i][child] == 'cop'):
for child2 in word_to_child[i][token_t]:
if (child != child2 and 'VB'
in word_to_pos[i][index_to_word[child2]]):
try:
sn.concept(index_to_word[token_t])
sn.concept(index_to_word[child2])
aspect_result[i].append(token_t)
aspect_result[i].append(child2)
if __name__ == '__main__':
print("rule 6 trigg")
print(token_t)
print(child2)
except KeyError:
pass
else:
"""
Rule 7:Very big to hold.
"""
for word in sent:
if ('RB' in word_to_pos[i][index_to_word[word]]
or 'JJ' in word_to_pos[i][index_to_word[word]]):
for child in word_to_child[i][word]:
if (word_to_dep[i][child] == 'xcomp'
or word_to_dep[i][child] == 'ccomp'):
aspect_result[i].append(word)
if __name__ == '__main__':
print("Rule 7 triggered")
print(word)
"""
Rule 8: Love the sleekness of the player.
"""
for word in sent:
for child in word_to_child[i][word]:
if ('NN' in word_to_pos[i][index_to_word[child]]
and word_to_dep[i][child] == 'nmod'):
for grandchild in word_to_child[i][child]:
if ('IN' in word_to_pos[i][
index_to_word[grandchild]]):
aspect_result[i].append(word)
aspect_result[i].append(child)
if __name__ == '__main__':
print(word)
print(child)
print("Rule 8 triggered.")
"""
Rule 9: Not to mention the price of the phone.
"""
for word in sent:
for child in word_to_child[i][word]:
if (word_to_dep[i][child] == 'dobj'):
aspect_result[i].append(child)
if __name__ == '__main__':
print(child)
print("Rule 9 triggered")
'''
Rule 11 : Checking for conjuctions
'''
for asp in aspect_result[i]:
for word in sent:
if (word_to_dep[i][word] == 'conj'
and word_to_par[i][word] == asp):
aspect_result[i].append(word)
if (__name__ == '__main__'):
print("Rule conj triggered.")
print(word)
finalIAC = [set(aspect_result[i]) for i in range(len(sents))]
finalIAC = [[index_to_word[w] for w in finalIAC[i]]
for i in range(len(sents))]
print(finalIAC)
singleFinalIAC = []
for i in range(len(sents)):
for w in finalIAC[i]:
if w not in stop_words:
singleFinalIAC.append(w)
print(singleFinalIAC)
finalSenti = []
for iac in singleFinalIAC:
try:
concept_info = sn.concept((iac))
finalSenti.append(iac)
except KeyError:
print("No word available for " + iac)
return singleFinalIAC, finalSenti
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
import nltk
nltk.download()
import os
from senticnet.senticnet import SenticNet
sn = SenticNet()
sn.concept('')
def fun1(d):
try:
from senticnet.senticnet import SenticNet
sn = SenticNet()
sn.semantics(d)
return True
except KeyError as error:
return False
fun1('day')
from nltk.corpus import wordnet
sk = wordnet.synset('ssd')
from senticnet.senticnet import SenticNet
sn = SenticNet('ru')
word = input('Введите ваш комментарий(например "как дела"): ')
lst = word.split()
#concept_info = sn.concept(word)
#polarity_value = sn.polarity_value(word)
#polarity_intense = sn.polarity_intense(word)
#moodtags = sn.moodtags(word)
#semantics = sn.semantics(word)
print(list(map(lambda x: sn.sentics(x), lst)))
pop = input(" ")
import bagofconcepts as boc
# Each line of corpus must be equivalent to each document of the corpus
#boc_model=boc.BOCModel(doc_path="input corpus path")
boc_model = boc.BOCModel('text.txt')
#boc_model.context = text
# output can be saved with save_path parameter
boc_matrix, word2concept_list, idx2word_converter = boc_model.fit()
# SenitcNet lexicon lookup
from senticnet.senticnet import SenticNet
sn = SenticNet()
concept_info = sn.concept(text)
polarity_value = sn.polarity_value(text)
polarity_intense = sn.polarity_intense(text)
moodtags = sn.moodtags(text)
semantics = sn.semantics(text)
sentics = sn.sentics(text)
print('==================================')
print('test: ', text)
print('concept_info: ', concept_info)
print('polarity_value: ', polarity_value)
print('polarity_intense: ', polarity_intense)
print('moodtags: ', moodtags)
print('semantics: ', semantics)
def data_Preprocessing(data, data_test, n_of_words, polarity_threshold):
Reviews = data["Content"]
#Check if all char are ASCII
# If we need another method for Encode/Decode there is string.printable method
for i in range(0, len(Reviews)):
x = Reviews.iloc[i].encode('ascii', errors='ignore').decode()
# Set all the content to lower case
Reviews = Reviews.apply(lambda row: row.lower())
# Add to the follow variable the characters that you want to delete
chars_to_del = "[" + string.punctuation + string.digits + "]"
# Delete all the chars in "chars_to_del" from each row of the dataframe
Reviews = Reviews.apply(lambda row: re.sub(chars_to_del, '', row))
# Tokenize every single words of the data content
Token_Reviews = Reviews.apply(lambda row: nltk.word_tokenize(row))
# Generating the list "stop" of element TO BE REMOVED from the sentences (stopwords, numbers and punctuations)
stop = stopwords.words("english")
# Remove all the words in the variable "stop"
Filtered_Review = Token_Reviews.apply(
lambda row: [w for w in row if not w in stop])
# Stemming the data's content
# Stemming the Filtered sentence, some stemmed words:
# http://snowball.tartarus.org/algorithms/english/stemmer.html
ps = PorterStemmer()
for idx in range(0, len(Filtered_Review)):
Stemmed_Review_temp = []
for word in Filtered_Review.iloc[i]:
Stemmed_Review_temp.append(ps.stem(word))
Filtered_Review.iloc[i] = Stemmed_Review_temp
# Terms choosing: most common word
sn = SenticNet()
Filtered_Review_List = list(itertools.chain.from_iterable(Filtered_Review))
Words_Frquency = FreqDist(Filtered_Review_List)
Most_Common_Words_Frequency = Words_Frquency.most_common(n_of_words)
Most_Common_Words = []
for i in range(0, n_of_words):
Most_Common_Words.append(Most_Common_Words_Frequency[i][0])
index = 1
words_and_polarity = pd.DataFrame(columns=["Word", "Polarity"])
Selected_Words = []
# Terms polarity
for word in Most_Common_Words:
try:
temp = sn.polarity_intense(word)
if (float(temp) > polarity_threshold
or float(temp) < -(polarity_threshold)):
words_and_polarity.loc[index] = [word, float(temp)]
index = index + 1
Selected_Words.append(word)
except Exception:
continue
# Decomment if you want to recomputer the selected words and their polarity
#words_and_polarity.to_csv("Words_and_Polarity.csv", sep=",")
return data, data_test
# for w in Dict.keys():
# if w not in porvalis and w not in slangwords and w not in kaggeleSentiment:
# print(w)
# c = c + 1
# print(c)
def Precision(tp, fp):
return tp / (tp + fp)
def Recall(tp, fn):
return tp / (tp + fn)
sn = SenticNet()
zeroSen = 0
tp = 0
tn = 0
fp = 0
fn = 0
actT = 0
with open("Dataset.pickle", "rb") as handle:
pyDS = pickle.load(handle)
for doc in pyDS.DocList:
totSen = 0
for w in doc.TermList:
try:
sen = sn.polarity_intense(w)
except KeyError:
sen = 0
import stanza as st
import csv
import textstat
import numpy as np
import matplotlib.pyplot as plt
import random
import copy
from gensim.models import KeyedVectors
from senticnet.senticnet import SenticNet
sn = SenticNet()
import language_tool_python
tool = language_tool_python.LanguageTool('en-US')
from transformers import pipeline
from wordhoard import antonyms,synonyms
from nltk import tokenize
from nltk.corpus import wordnet
from nltk.tokenize.treebank import TreebankWordDetokenizer
nlp = pipeline("fill-mask",model="bert-large-uncased",tokenizer="bert-large-uncased")
surname = np.load('surname.npy', allow_pickle=True)
malenames = np.load('male.npy', allow_pickle=True)
femalenames = np.load('female.npy', allow_pickle=True)
unsex = np.load('unsex.npy', allow_pickle=True)
boy = np.load('boy.npy', allow_pickle=True)
girl = np.load('girl.npy', allow_pickle=True)
gender_word=[['boy', 'girl'], ['boys', 'girls'], ['nephew', 'niece'], ['brother', 'sister'], ['brothers', 'sisters'],['boyfriend', 'girlfriend'],
['dad', 'mom'], ['father', 'mother'], ['grandfather', 'grandmother'], ['grandpa', 'grandma'], ['grandson', 'granddaughter'], ['male','female'],
['groom', 'bride'], ['husband', 'wife'], ['king', 'queen'], ['man', 'woman'],['men','women'], ['policeman', 'policewoman'], ['prince', 'princess'],
from senticnet.senticnet import SenticNet
sn = SenticNet()
print("polarity value:", sn.polarity_value("love"))
print("polarity intense:", sn.polarity_intense("love"))
print("moodtags:", ", ".join(sn.moodtags("love")))
print("semantics:", ", ".join(sn.semantics("love")))
print("\n".join([key + ": " + str(value) for key, value in sn.sentics("love").items()]))
# Классификация текстов по 5 шкалам согласно базе SenticNet, оценка XGBoost
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from senticnet.senticnet import SenticNet
from nltk.stem import WordNetLemmatizer
from sklearn import metrics
import xgboost
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
df = pd.read_csv('preprocessed.csv')
lemmatizer = WordNetLemmatizer() # преобразование слов в исходную форму
sn = SenticNet() # база данных, содержащая классификацию слов и выражений по значению и настроению
# Функция, возвращающая оценки введенного текста по 5 шкалам базы SenticNet: polarity intensity,
# pleasantness, attention, sensitivity и aptitude. Оценка формируется как сумма оценок
# всех включенных в базу слов и выражений из текста и нормируется на кол-во слов в тексте
def SN(data):
# Преобразование текста в вектор, формирование словаря слов и словосочетаний длинной до 3 слов включительно
vectorizer = CountVectorizer(analyzer='word', ngram_range=(1,3))
vec = vectorizer.fit_transform([data]).todense()
k = 0
polarity_intense = sentics_pleasant = sentics_attention = sentics_sense = sentics_aptitude = 0
for i in vectorizer.vocabulary_.keys():
try: # Попытка найти i-ое слово/выражение в базе
num_repetitions = vec[0, vectorizer.vocabulary_[i]]
polarity_intense += (float(sn.polarity_intense(i)) * num_repetitions)
sentics_pleasant += (float(sn.sentics(i)['pleasantness']) * num_repetitions)
def __init__(self):
self.col = ['Name', 'Brand', 'Price', 'Title', 'Score', 'Time', 'Text']
self.sn = SenticNet('en')
self.wordnet_lemmatizer = WordNetLemmatizer()
class Sarcasm:
def __init__(self, *args, **kwargs):
# loading necessaries
self.nlp = spacy.load("en_core_web_sm")
self.senti = SenticNet()
self.sid = SentimentIntensityAnalyzer()
#loading dataset
self.df = pd.read_json("./Sarcasm_Headlines_Dataset.json", lines=True)
self.df = self.df[:15000]
self.df.drop(columns="article_link",
inplace=True) #dropping unnessary attribute
#storing nlp data in headlines variable
self.headlines = []
self.uni_gram = set()
self.uni_feature = []
self.y_ = []
for i in self.df['headline']:
self.headlines.append(self.nlp(i))
def w_score(self, w):
"""
input: word
Finding word score based on nltk's vader_lexicon sentiment analysis
and Senticnet sentiment analysis
"""
ss = self.sid.polarity_scores(w)['compound']
try:
sn = self.senti.polarity_intense(w)
sn = float(sn)
if ss == 0:
return sn
else:
return (sn + ss) / 2
except:
#not found in sn find for only ss or concepts
if ss != 0:
return ss
elif ss == 0: #find for the concepts
return ss
def sentimentScore(self, sent):
"""
input: sentence
Return if contradiction occurs
or not
"""
sum_pos_score = 0
sum_neg_score = 0
for w in sent:
if w.lemma_ == '-PRON-':
score = self.w_score(w.text)
else:
score = self.w_score(w.lemma_)
if score > 0:
sum_pos_score += score
else:
sum_neg_score += score
if sum_pos_score > 0 and sum_neg_score < 0:
return ("contradict", sum_pos_score, sum_neg_score)
else:
return ("anything", sum_pos_score, sum_neg_score)
def coherence(self, s1, s2):
'''
Input sentence1, sentence2 using nlp
Rule1:- Pronoun match feature - including reflexive, personal, and possessive pronouns.
Rule2:- String match feature - ignore stop words
Rule3:- Definite noun phrase - w2 starts with the word 'the'
Rule4:- Demonstrative noun phrase feature - w2 starts with the "this", "that", "these" and "those"
Rule5:- Both proper names features - w1 and w2 are both named entities
'''
# subject and object of s1 and s2
sub1 = ""
sub2 = ""
obj1 = ""
obj2 = ""
for i in s1.noun_chunks:
if i.root.dep_ == 'nsubj':
sub1 = i.root
if i.root.dep == 'pobj':
obj1 = i.root
for j in s2.noun_chunks:
if j.root.dep_ == 'nsubj':
if type(sub1) != type(
"") and sub1.pos_ == 'PRON' and j.root.pos_ == 'PRON':
if sub1.text.lower() == j.root.text.lower():
return "coherent"
# rule 4:-
if j[0].text.lower() == 'the':
return "coherent"
if j[0].text.lower() in ['this', 'that', 'these', 'those']:
return "coherent"
if j.root.dep_ == 'pobj':
if type(obj1) != type(
"") and obj1.pos_ == 'PRON' and j.root.pos_ == 'PRON':
if obj1.text.lower() == j.root.text.lower():
return "coherent"
return "Not coherent"
def to_string_from_list(self, l):
st = ""
for i in l:
st += i + ' '
return st.rstrip()
def n_gram_feature(self, text, n):
"""
Input: headline in nlp
Finding n grams of given text
"""
one_list = []
for tok in text:
if not tok.is_punct:
if tok.lemma_ != '-PRON-':
one_list.append(tok.lemma_)
else:
one_list.append(tok.text)
try:
one_list.remove(' ')
except:
pass
#convert it to n-gram
_list = []
for i, t in enumerate(one_list):
if len(one_list[i:n + i]) >= n:
_list.append(self.to_string_from_list(one_list[i:n + i]))
return set(_list)
def contradiction_feature(self, headline):
'''
Contradiction feature
input: nlp processed
'''
#for single sentence headline
if len(list(headline.sents)) == 1:
if self.sentimentScore(headline)[0] == 'contradict':
return (1, 0)
else:
return (0, 0)
#for multisentence headline
else:
if self.sentimentScore(headline)[0] == 'contradict':
sent = list(headline.sents)
i = 0
while i < len(sent) - 1:
# number of sentece
if self.coherence(sent[i], sent[i + 1]) is not "coherent":
return (0, 0)
i += 1
return (0, 1)
else:
return (0, 0)
def baseline3(self):
'''
Use of sentiment analysis + coherence
'''
predictions = []
for i in self.headlines:
get = self.contradiction_feature(i)
if get == (1, 0) or get == (0, 1):
predictions.append(1)
else:
predictions.append(0)
return (confusion_matrix(self.df['is_sarcastic'], predictions),
classification_report(self.df['is_sarcastic'], predictions),
accuracy_score(self.df['is_sarcastic'], predictions))
def baseline1(self):
predictions = []
for p in self.headlines:
co, _, _ = self.sentimentScore(p)
if (co == 'contradict'):
predictions.append(1)
else:
predictions.append(0)
return (confusion_matrix(self.df['is_sarcastic'], predictions),
classification_report(self.df['is_sarcastic'], predictions),
accuracy_score(self.df['is_sarcastic'], predictions))
def uni_gram_features(self, start, end, n=1):
self.uni_gram = list(self.uni_gram)
self.uni_gram = sorted(self.uni_gram)
index = start
for p in self.headlines[start:end]:
uni = [0 for i in range(len(self.uni_gram))]
for i, j in enumerate(p):
temp = [] #temp
if len(p[i:n + i]) >= n:
for k in range(n):
if p[i + k].lemma_ != '-PRON-':
temp.append(p[i + k].lemma_)
else:
temp.append(p[i + k].text)
temp = self.to_string_from_list(temp)
if temp in self.uni_gram:
uni[self.uni_gram.index(temp)] = 1
self.y_.append(self.df['is_sarcastic'][index])
index += 1
self.uni_feature.append(uni)
def baseline2(self, n=1):
#unigram features
self.uni_gram = set()
self.uni_feature = []
self.y_ = []
for p in self.headlines:
self.uni_gram = self.uni_gram.union(self.n_gram_feature(p, n))
#now find
length = len(self.headlines)
t1 = threading.Thread(target=self.uni_gram_features,
name='t1',
args=(0, int(length / 4), n))
t2 = threading.Thread(target=self.uni_gram_features,
name='t2',
args=(int(length / 4), int(length / 2), n))
t3 = threading.Thread(target=self.uni_gram_features,
name='t3',
args=(int(length / 2), int(3 * length / 4), n))
t4 = threading.Thread(target=self.uni_gram_features,
name='t4',
args=(int(3 * length / 4), length, n))
t1.daemon = True
t2.daemon = True
t3.daemon = True
t4.daemon = True
st = time.time()
t1.start()
t2.start()
t3.start()
t4.start()
t1.join()
t2.join()
t3.join()
t4.join()
print(f'time taken: {time.time()-st}')
X_train, X_test, y_train, y_test = train_test_split(self.uni_feature,
self.y_,
test_size=0.33,
random_state=42)
return self.findLINEARSVCResult(X_train, X_test, y_train, y_test)
def findLINEARSVCResult(self, X_train, X_test, y_train, y_test):
'''
Training data using LinearSVC model
'''
svc_model = LinearSVC()
svc_model.fit(X_train, y_train)
predictions = svc_model.predict(X_test)
return (confusion_matrix(y_test, predictions),
classification_report(y_test, predictions),
accuracy_score(y_test, predictions))
