def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
if self.sentiwordnet:
print "using sentiwordnet dictionary"
else:
print "not using sentiwordnet dictionary"
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(
os.path.join(os.curdir, os.path.normpath('../data/' + data_file)),
'rb')
self.limit['en'] = 300000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/' + conf.SENTIWORDNET_DICT_FILENAME
self.swn = SentiWordNetCorpusReader(swn_filename)
def findScoreWord(word, dType):
swn_filename = 'SentiWordNet_3.0.0_20130122.txt'
swn = SentiWordNetCorpusReader(swn_filename)
word = re.sub('[%s]' % re.escape(string.punctuation), ' ', word)
word = word.lower()
#print word
wS = 0
for w in word.split():
#print w
if dType == NN:
test = swn.senti_synsets(w, 'n')
elif dType == ADJ:
test = swn.senti_synsets(w, 'a')
elif dType == VB:
test = swn.senti_synsets(w, 'v')
elif dType == ADV:
test = swn.senti_synsets(w, 'r')
try:
if test[0].pos_score < 0.1:
wS += -test[0].neg_score
elif test[0].neg_score < 0.1:
wS += test[0].pos_score
else:
wS = test[0].pos_score
except:
continue
#print word, wS
if len(word.split()) == 0:
return 0
return wS/len(word.split())
def __init__(self):
self.mwnet = MWNet(os.path.join("data", "mwnet.db"))
self.swn = SentiWordNetCorpusReader(os.path.join("data", "SentiWordNet_3.0.0.txt"))
if os.getenv('TANL_EMAIL'):
self.splitter = TanlSplitter()
else:
self.splitter = SimpleSplitter()
def __init__(self,
traing_data_fileP1='mood_traing_p1.dat',
traing_data_fileP2='mood_traing.dat',
data_file='tweets_raw.dat'):
self.clsP1 = MoodDetectTrainer(data_file=traing_data_fileP1)
self.clsP2 = MoodDetectTrainer(data_file=traing_data_fileP2)
self.langClassifier = LangDetect(supportedLangs)
self.training_data_p1 = MoodDetectTrainData()
self.training_data_p2 = MoodDetectTrainData()
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.countRows(self.tweetsFile)
self.tweetsFile = open(os.path.join(self.dataDir, data_file), 'rb')
self.limit['en'] = 150000
self.limit['default'] = 10000
self.count = 0
swn_filename = '../dict/sentiwordnet/SentiWordNet_3.0.0_20100705.txt'
self.swn = SentiWordNetCorpusReader(swn_filename)
words_noslang = []
for w in words:
for k, v in dico_slang.items():
if k in w:
#print(k)
w = list(filter(lambda x: x != k, w))
w.append(v)
#print(w)
words_noslang.append(w)
print(words_noslang)
tag = nltk.pos_tag
tag_list_tot = []
tag_list = [tag(words_noslang[i]) for i in range(N)]
print(tag_list)
for i in range(N):
tag_list_tot.extend(tag(words_noslang[i]))
tot_pos = [tag_list_tot[i][1] for i in range(len(tag_list_tot))]
count_VB = Counter(tot_pos)
print(count_VB)
verb_tag = ['VB', 'VBZ', 'VBP', 'VBG', 'VBN', 'VBD']
sum_vb = 0
for k, v in count_VB.items():
if k in verb_tag:
sum_vb += v
print('Le nombre total de POS verbes sur l\'ensemble des tweets est: ', sum_vb)
swn_filename = '/home/audrey/Audrey/Cours/INF344/TP/TP_sentiment/SentiWordNet_3.0.0_20130122.txt'
swn = SentiWordNetCorpusReader(swn_filename)
swn.senti_synset('breakdown.n.03')
def sentimentAnalysis(filename, outputFile):
swn_filename = 'SentiWordNet_3.0.0_20100705.txt'
swn = SentiWordNetCorpusReader(swn_filename)
regex = re.compile('[%s]' % re.escape(string.punctuation))
with codecs.open(filename, 'r', 'utf-8') as f:
data1 = f.readlines()
f.close()
writer = csv.writer(open(outputFile, 'wb'))
tag = ['CC', 'CD', 'DT', 'EX', 'FW', 'IN', 'TO', 'UH', 'PDT', 'SYM', 'RP']
noun = ['NN', 'NNS', 'NP', 'NPS']
adj = ['JJ', 'JJR', 'JJS']
pronoun = ['PP', 'PP$', 'WP', 'WP$']
verb = ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ']
adverb = ['RB', 'RBR', 'RBS', 'WRB']
for text in data1:
count = re.split("\s+", text, 1)[0]
if len(re.split("\s+", text, 1)) > 1:
text = re.split("\s+", text, 1)[1]
Tex = regex.sub(u'', text)
words = word_tokenize(Tex.lower())
word = nltk.pos_tag(words)
objCount = 0
subCount = 0
for w in word:
if not w[1] in tag:
#print(w)
if w[1] in noun:
pos_Char = 'n'
elif w[1] in adj:
pos_Char = 'a'
elif w[1] in pronoun:
pos_Char = 'p'
elif w[1] in verb:
pos_Char = 'v'
elif w[1] in adverb:
pos_Char = 'r'
else:
pos_Char = 'none'
if pos_Char == 'none':
try:
s = swn.senti_synsets(w[0])
scores = list(s)[0]
if scores.obj_score > 0.5:
objCount += 1
elif scores.pos_score + scores.neg_score > 0.5:
subCount += 1
except:
print('Unexpected word')
else:
try:
s = swn.senti_synsets(w[0], pos_Char)
scores = list(s)[0]
if scores.obj_score > 0.5:
objCount += 1
elif scores.pos_score + scores.neg_score > 0.5:
subCount += 1
except:
print('Unexpected word')
if objCount + subCount > 0:
ratioObj = float(objCount) / (objCount + subCount)
ratioSub = float(subCount) / (objCount + subCount)
else:
ratioObj = 0.0
ratioSub = 0.0
writer.writerow([count, ratioObj, ratioSub])
