def pos_feature(features, sentence):
sentence_pos = exp_replace.replace_emo(sentence)
tokens = nltk.word_tokenize(sentence_pos)
tokens = [(t.lower()) for t in tokens]
pos_vector = sentiments.posvector(tokens)
for j in range(len(pos_vector)):
features["POS" + str(j + 1)] = pos_vector[j]
def pos_feature(features, sentence):
sentence_pos = exp_replace.replace_emo(sentence)
tokens = nltk.word_tokenize(sentence_pos)
tokens = [(t.lower()) for t in tokens]
pos_vector = sentiments.posvector(tokens)
for j in range(len(pos_vector)):
features['POS' + str(j + 1)] = pos_vector[j]
def sent_feature(features, sentence):
sentence_sentiment = exp_replace.replace_emo(sentence) ## :) is replaced by good and :( is replaced by sad
tokens = nltk.word_tokenize(sentence_sentiment)
tokens = [(t.lower()) for t in tokens]
mean_sentiment = sentiments.score_sentence(tokens)
features['Positive sentiment'] = mean_sentiment[0]
features['Negative sentiment'] = mean_sentiment[1]
features['Sentiment'] = mean_sentiment[0] - mean_sentiment[1]
# TextBlob sentiment analysis for full sentence
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in tokens]).strip())
features['BlobSentiment'] = blob.sentiment.polarity
except:
features['BlobSentiment'] = 0.0
# Splitting the sentence into 2 parts and then calculating sentiment analysis on the sentence
if len(tokens) == 1:
tokens += ['.']
f_half = tokens[0:len(tokens) / 2]
s_half = tokens[len(tokens) / 2:]
mean_sentiment_f = sentiments.score_sentence(f_half)
features['PosSentiment1/2'] = mean_sentiment_f[0]
features['Negsentiment1/2'] = mean_sentiment_f[1]
features['Sentiment1/2'] = mean_sentiment_f[0] - mean_sentiment_f[1]
mean_sentiment_s = sentiments.score_sentence(s_half)
features['PosSentiment2/2'] = mean_sentiment_s[0]
features['NegSentiment2/2'] = mean_sentiment_s[1]
features['Sentiment2/2'] = mean_sentiment_s[0] - mean_sentiment_s[1]
features['SentimentContrast'] = np.abs(features['Sentiment 1/2'] - features['Sentiment 2/2'])
# TextBlob sentiment analysis for bith the halves
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in f_half]).strip())
features['BlobSentiment1/2'] = blob.sentiment.polarity
except:
features['BlobSentiment1/2'] = 0.0
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in s_half]).strip())
features['BlobSentiment2/2'] = blob.sentiment.polarity
except:
features['BlobSentiment2/2'] = 0.0
features['BlobSubjectivity2/2'] = 0.0
features['BlobSentimentContrast'] = np.abs(features['BlobSentiment1/2'] - features['BlobSentiment2/2'])
def sent_feature(features, sentence):
sentence_sentiment = exp_replace.replace_emo(sentence)
tokens = nltk.word_tokenize(sentence_sentiment)
tokens = [(t.lower()) for t in tokens]
mean_sentiment = sentiments.score_sentence(tokens)
features['Positive sentiment'] = mean_sentiment[0]
features['Negative sentiment'] = mean_sentiment[1]
features['Sentiment'] = mean_sentiment[0] - mean_sentiment[1]
#TextBlob sentiment analysis
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in tokens
]).strip())
features['Blob sentiment'] = blob.sentiment.polarity
features['Blob subjectivity'] = blob.sentiment.subjectivity
except:
features['Blob sentiment'] = 0.0
features['Blob subjectivity'] = 0.0
#Split in 2
if len(tokens) == 1:
tokens += ['.']
f_half = tokens[0:len(tokens) / 2]
s_half = tokens[len(tokens) / 2:]
mean_sentiment_f = sentiments.score_sentence(f_half)
features['Positive sentiment 1/2'] = mean_sentiment_f[0]
features['Negative sentiment 1/2'] = mean_sentiment_f[1]
features['Sentiment 1/2'] = mean_sentiment_f[0] - mean_sentiment_f[1]
mean_sentiment_s = sentiments.score_sentence(s_half)
features['Positive sentiment 2/2'] = mean_sentiment_s[0]
features['Negative sentiment 2/2'] = mean_sentiment_s[1]
features['Sentiment 2/2'] = mean_sentiment_s[0] - mean_sentiment_s[1]
features['Sentiment contrast 2'] = np.abs(features['Sentiment 1/2'] -
features['Sentiment 2/2'])
#TextBlob sentiment analysis
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in f_half
]).strip())
features['Blob sentiment 1/2'] = blob.sentiment.polarity
features['Blob subjectivity 1/2'] = blob.sentiment.subjectivity
except:
features['Blob sentiment 1/2'] = 0.0
features['Blob subjectivity 1/2'] = 0.0
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in s_half
]).strip())
features['Blob sentiment 2/2'] = blob.sentiment.polarity
features['Blob subjectivity 2/2'] = blob.sentiment.subjectivity
except:
features['Blob sentiment 2/2'] = 0.0
features['Blob subjectivity 2/2'] = 0.0
features['Blob Sentiment contrast 2'] = np.abs(
features['Blob sentiment 1/2'] - features['Blob sentiment 2/2'])
#Split in 3
if len(tokens) == 2:
tokens += ['.']
f_half = tokens[0:len(tokens) / 3]
s_half = tokens[len(tokens) / 3:2 * len(tokens) / 3]
t_half = tokens[2 * len(tokens) / 3:]
mean_sentiment_f = sentiments.score_sentence(f_half)
features['Positive sentiment 1/3'] = mean_sentiment_f[0]
features['Negative sentiment 1/3'] = mean_sentiment_f[1]
features['Sentiment 1/3'] = mean_sentiment_f[0] - mean_sentiment_f[1]
mean_sentiment_s = sentiments.score_sentence(s_half)
features['Positive sentiment 2/3'] = mean_sentiment_s[0]
features['Negative sentiment 2/3'] = mean_sentiment_s[1]
features['Sentiment 2/3'] = mean_sentiment_s[0] - mean_sentiment_s[1]
mean_sentiment_t = sentiments.score_sentence(t_half)
features['Positive sentiment 3/3'] = mean_sentiment_t[0]
features['Negative sentiment 3/3'] = mean_sentiment_t[1]
features['Sentiment 3/3'] = mean_sentiment_t[0] - mean_sentiment_t[1]
features['Sentiment contrast 3'] = np.abs(features['Sentiment 1/3'] -
features['Sentiment 3/3'])
#TextBlob sentiment analysis
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in f_half
]).strip())
features['Blob sentiment 1/3'] = blob.sentiment.polarity
features['Blob subjectivity 1/3'] = blob.sentiment.subjectivity
except:
features['Blob sentiment 1/3'] = 0.0
features['Blob subjectivity 1/3'] = 0.0
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in s_half
]).strip())
features['Blob sentiment 2/3'] = blob.sentiment.polarity
features['Blob subjectivity 2/3'] = blob.sentiment.subjectivity
except:
features['Blob sentiment 2/3'] = 0.0
features['Blob subjectivity 2/3'] = 0.0
try:
blob = TextBlob("".join([
" " +
i if not i.startswith("'") and i not in string.punctuation else i
for i in t_half
]).strip())
features['Blob sentiment 3/3'] = blob.sentiment.polarity
features['Blob subjectivity 3/3'] = blob.sentiment.subjectivity
except:
features['Blob sentiment 3/3'] = 0.0
features['Blob subjectivity 3/3'] = 0.0
features['Blob Sentiment contrast 3'] = np.abs(
features['Blob sentiment 1/3'] - features['Blob sentiment 3/3'])
def sent_feature(features, sentence):
sentence_sentiment = exp_replace.replace_emo(sentence)
tokens = nltk.word_tokenize(sentence_sentiment)
tokens = [(t.lower()) for t in tokens]
mean_sentiment = sentiments.score_sentence(tokens)
features["Positive sentiment"] = mean_sentiment[0]
features["Negative sentiment"] = mean_sentiment[1]
features["Sentiment"] = mean_sentiment[0] - mean_sentiment[1]
# TextBlob sentiment analysis
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in tokens]).strip()
)
features["Blob sentiment"] = blob.sentiment.polarity
features["Blob subjectivity"] = blob.sentiment.subjectivity
except:
features["Blob sentiment"] = 0.0
features["Blob subjectivity"] = 0.0
# Split in 2
if len(tokens) == 1:
tokens += ["."]
f_half = tokens[0 : len(tokens) / 2]
s_half = tokens[len(tokens) / 2 :]
mean_sentiment_f = sentiments.score_sentence(f_half)
features["Positive sentiment 1/2"] = mean_sentiment_f[0]
features["Negative sentiment 1/2"] = mean_sentiment_f[1]
features["Sentiment 1/2"] = mean_sentiment_f[0] - mean_sentiment_f[1]
mean_sentiment_s = sentiments.score_sentence(s_half)
features["Positive sentiment 2/2"] = mean_sentiment_s[0]
features["Negative sentiment 2/2"] = mean_sentiment_s[1]
features["Sentiment 2/2"] = mean_sentiment_s[0] - mean_sentiment_s[1]
features["Sentiment contrast 2"] = np.abs(features["Sentiment 1/2"] - features["Sentiment 2/2"])
# TextBlob sentiment analysis
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in f_half]).strip()
)
features["Blob sentiment 1/2"] = blob.sentiment.polarity
features["Blob subjectivity 1/2"] = blob.sentiment.subjectivity
except:
features["Blob sentiment 1/2"] = 0.0
features["Blob subjectivity 1/2"] = 0.0
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in s_half]).strip()
)
features["Blob sentiment 2/2"] = blob.sentiment.polarity
features["Blob subjectivity 2/2"] = blob.sentiment.subjectivity
except:
features["Blob sentiment 2/2"] = 0.0
features["Blob subjectivity 2/2"] = 0.0
features["Blob Sentiment contrast 2"] = np.abs(features["Blob sentiment 1/2"] - features["Blob sentiment 2/2"])
# Split in 3
if len(tokens) == 2:
tokens += ["."]
f_half = tokens[0 : len(tokens) / 3]
s_half = tokens[len(tokens) / 3 : 2 * len(tokens) / 3]
t_half = tokens[2 * len(tokens) / 3 :]
mean_sentiment_f = sentiments.score_sentence(f_half)
features["Positive sentiment 1/3"] = mean_sentiment_f[0]
features["Negative sentiment 1/3"] = mean_sentiment_f[1]
features["Sentiment 1/3"] = mean_sentiment_f[0] - mean_sentiment_f[1]
mean_sentiment_s = sentiments.score_sentence(s_half)
features["Positive sentiment 2/3"] = mean_sentiment_s[0]
features["Negative sentiment 2/3"] = mean_sentiment_s[1]
features["Sentiment 2/3"] = mean_sentiment_s[0] - mean_sentiment_s[1]
mean_sentiment_t = sentiments.score_sentence(t_half)
features["Positive sentiment 3/3"] = mean_sentiment_t[0]
features["Negative sentiment 3/3"] = mean_sentiment_t[1]
features["Sentiment 3/3"] = mean_sentiment_t[0] - mean_sentiment_t[1]
features["Sentiment contrast 3"] = np.abs(features["Sentiment 1/3"] - features["Sentiment 3/3"])
# TextBlob sentiment analysis
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in f_half]).strip()
)
features["Blob sentiment 1/3"] = blob.sentiment.polarity
features["Blob subjectivity 1/3"] = blob.sentiment.subjectivity
except:
features["Blob sentiment 1/3"] = 0.0
features["Blob subjectivity 1/3"] = 0.0
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in s_half]).strip()
)
features["Blob sentiment 2/3"] = blob.sentiment.polarity
features["Blob subjectivity 2/3"] = blob.sentiment.subjectivity
except:
features["Blob sentiment 2/3"] = 0.0
features["Blob subjectivity 2/3"] = 0.0
try:
blob = TextBlob(
"".join([" " + i if not i.startswith("'") and i not in string.punctuation else i for i in t_half]).strip()
)
features["Blob sentiment 3/3"] = blob.sentiment.polarity
features["Blob subjectivity 3/3"] = blob.sentiment.subjectivity
except:
features["Blob sentiment 3/3"] = 0.0
features["Blob subjectivity 3/3"] = 0.0
features["Blob Sentiment contrast 3"] = np.abs(features["Blob sentiment 1/3"] - features["Blob sentiment 3/3"])
def extractFeatureOfASentence(sen):
"""
This method extracts features of a single sentence.
We have following list of features being extracted.
1. Full sentence Polarity
2. Full sentence Subjectivity
3. Half sentence Polarity (1/2 and 2/2)
4. Half sentence Subjectivity (1/2 and 2/2)
5. Difference between polarities of two halves
6. Third sentence Polarity (1/3, 2/3 and 3/3)
7. Third sentence Subjectivity (1/3, 2/3 and 3/3)
8. Difference between max and min polarity of the thirds.
9. Fourth sentence Polarity (1/4, 2/4, 3/4 and 4/4)
10. Fourth sentence Subjectivity (1/4, 2/4, 3/4 and 4/4)
11. Difference between max and min polarities of the fourths.
Like this we extract 23 features of a single sentence.
:param sen:
:return:
"""
features = []
# adding capitalization feature
counter = 0
threshold = 4
sentence_plain = sen.decode('UTF-8')
for j in range(len(sentence_plain)):
counter += int(sentence_plain[j].isupper())
features.append(counter)
# end of adding capitalization feature
# Tokenize the sentence and then convert everthing to lower case.
tokens = nltk.word_tokenize(exp_replace.replace_emo(str(sen)))
tokens = [(t.lower()) for t in tokens]
# Adding pos_feature
pos_vector = posvector(tokens)
for j in range(len(pos_vector)):
features.append(pos_vector[j])
# End of adding pos_feature
# Extract features of full sentence.
fullBlob = TextBlob(joinTokens(tokens))
features.append(fullBlob.sentiment.polarity)
features.append(fullBlob.sentiment.subjectivity)
# Extract features of halves.
size = len(tokens) // 2
parts = []
i = 0
while i <= len(tokens):
if i == size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
for x in range(0, len(parts)):
part = parts[x]
halfBlob = TextBlob(joinTokens(part))
features.append(halfBlob.sentiment.polarity)
features.append(halfBlob.sentiment.subjectivity)
features.append(np.abs(features[-2] - features[-4]))
# Extract features of thirds.
size = len(tokens) // 3
parts = []
i = 0
while i <= len(tokens):
if i == 2 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
thirdsBlob = TextBlob(joinTokens(part))
pol = thirdsBlob.sentiment.polarity
sub = thirdsBlob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
# Extract features of fourths.
size = len(tokens) // 4
parts = []
i = 0
while i <= len(tokens):
if i == 3 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
fourthsBlob = TextBlob(joinTokens(part))
pol = fourthsBlob.sentiment.polarity
sub = fourthsBlob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
return features
def extractFeatureOfASentence(sen):
features = []
# Tokenize the sentence and then convert everthing to lower case.
tokens = nltk.word_tokenize(exp_replace.replace_emo(str(sen)))
tokens = [(t.lower()) for t in tokens]
# Extract features of full sentence.
fullBlob = TextBlob(joinTokens(tokens))
features.append(fullBlob.sentiment.polarity)
features.append(fullBlob.sentiment.subjectivity)
# Extract features of halves.
size = len(tokens) // 2
parts = []
i = 0
while i <= len(tokens):
if i == size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
for x in range(0, len(parts)):
part = parts[x]
halfBlob = TextBlob(joinTokens(part))
features.append(halfBlob.sentiment.polarity)
features.append(halfBlob.sentiment.subjectivity)
features.append(np.abs(features[-2] - features[-4]))
# Extract features of thirds.
size = len(tokens) // 3
parts = []
i = 0
while i <= len(tokens):
if i == 2 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
thirdsBlob = TextBlob(joinTokens(part))
pol = thirdsBlob.sentiment.polarity
sub = thirdsBlob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
# Extract features of fourths.
size = len(tokens) // 4
parts = []
i = 0
while i <= len(tokens):
if i == 3 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
fourthsBlob = TextBlob(joinTokens(part))
pol = fourthsBlob.sentiment.polarity
sub = fourthsBlob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
return features
def extract_feature_of_sentence(self, sen):
# type: (object) -> object
"""
This method extracts features of a single sentence.
We have following list of features being extracted.
1. Full sentence Polarity
2. Full sentence Subjectivity
3. Half sentence Polarity (1/2 and 2/2)
4. Half sentence Subjectivity (1/2 and 2/2)
5. Difference between polarities of two halves
6. Third sentence Polarity (1/3, 2/3 and 3/3)
7. Third sentence Subjectivity (1/3, 2/3 and 3/3)
8. Difference between max and min polarity of the thirds.
9. Fourth sentence Polarity (1/4, 2/4, 3/4 and 4/4)
10. Fourth sentence Subjectivity (1/4, 2/4, 3/4 and 4/4)
11. Difference between max and min polarities of the fourths.
Like this we extract 23 features of a single sentence.
:param sen:
:return:
"""
features = []
# Tokenize the sentence and then convert everything to lower case.
tokens = nltk.word_tokenize(exp_replace.replace_emo(str(sen)))
tokens = [(t.lower()) for t in tokens]
# Extract features of full sentence.
fullBlob = TextBlob(self.join_tokens(tokens))
features.append(fullBlob.sentiment.polarity)
features.append(fullBlob.sentiment.subjectivity)
# Extract features of halves.
size = len(tokens) // 2
parts = []
i = 0
while i <= len(tokens):
if i == size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
for x in range(0, len(parts)):
part = parts[x]
halfBlob = TextBlob(self.join_tokens(part))
features.append(halfBlob.sentiment.polarity)
features.append(halfBlob.sentiment.subjectivity)
features.append(np.abs(features[-2] - features[-4]))
# Extract features of thirds.
size = len(tokens) // 3
parts = []
i = 0
while i <= len(tokens):
if i == 2 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
thirdsBlob = TextBlob(self.join_tokens(part))
pol = thirdsBlob.sentiment.polarity
sub = thirdsBlob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
# Extract features of fourths.
size = len(tokens) // 4
parts = []
i = 0
while i <= len(tokens):
if i == 3 * size:
parts.append(tokens[i:])
break
else:
parts.append(tokens[i:i + size])
i += size
ma = -2
mi = 2
for x in range(0, len(parts)):
part = parts[x]
fourths_blob = TextBlob(self.join_tokens(part))
pol = fourths_blob.sentiment.polarity
sub = fourths_blob.sentiment.subjectivity
if pol > ma:
ma = pol
if pol < mi:
mi = pol
features.append(pol)
features.append(sub)
features.append(np.abs(ma - mi))
return features
