def __init__(self):
self.pos_tagger = SequentialTagger()
self.hp_obj = HpObj(debug=DEBUG)
self.hp_subj = HpSubj(debug=DEBUG)
self.lexicon = self.hp_obj.lexicon
self.bootstrapping = Bootstrapping(self.hp_obj, self.hp_subj, self.pos_tagger, debug=DEBUG)
self.sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
self.total_sentences = ["good","bad"]
self.total_sentiments = ["positive","negative"]
def __init__(self):
self.pos_tagger = SequentialTagger()
self.hp_obj = HpObj(debug=DEBUG)
self.hp_subj = HpSubj(debug=DEBUG)
self.lexicon = self.hp_obj.lexicon
self.bootstrapping = Bootstrapping(self.hp_obj, self.hp_subj, self.pos_tagger, debug=DEBUG)
self.sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
self.total_sentences = ["good","bad"]
self.total_sentiments = ["positive","negative"]
class Sentiment:
"""
Sentiment: Analyses the global sentiment of given text regions
that are decomposed to sentences, using bootstrapping methods for
subjectivity and polarity classification. All sub modules except
from POS tagging are learning by experience.
"""
def __init__(self):
self.pos_tagger = SequentialTagger()
self.hp_obj = HpObj(debug=DEBUG)
self.hp_subj = HpSubj(debug=DEBUG)
self.lexicon = self.hp_obj.lexicon
self.bootstrapping = Bootstrapping(self.hp_obj, self.hp_subj, self.pos_tagger, debug=DEBUG)
self.sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
self.total_sentences = ["good","bad"]
self.total_sentiments = ["positive","negative"]
def analyze(self, clean_text_areas):
"""
Analysis of text regions using the following order: Each sentence per
region is passed from the subjectivity classification using bootstrapping
method and then if it turns out to be subjective it is passed
from the polarity classification using bootstrapping method also.
Finally, it results to a decision for the sentiment of the sentence
and the overall sentiment of the regions.
"""
if len(clean_text_areas) > 0:
for clean_text in clean_text_areas:
# Sentence detection
clean_text = self.normalize(clean_text)
try:
sentences = self.sentence_tokenizer.tokenize(clean_text)
except:
return {}
sentiments = []
scores = []
nscores = []
results = {'positive':{'count' : 0, 'score' : 0, 'nscore' : 0},
'neutral':{'count' : 0, 'score' : 0, 'nscore' : 0},
'negative':{'count' : 0, 'score' : 0, 'nscore' : 0}}
print
print Tcolors.ACT + " Checking block of text:"
for i, sentence in enumerate(sentences):
print "[" + str(i+1) + "] " + sentence
for i, sentence in enumerate(sentences):
# Proceed to subjectivity classification (bootstrapping procedure).
# (This step could be skipped in case you deal with subjective sentences only.)
sentiment = ""
previous = ""
next = ""
score = 0
nscore = 0
if i == 0 and i + 1 < len(sentences):
next = sentences[i+1]
elif i != 0 and i < len(sentences):
if i + 1 != len(sentences):
next = sentences[i+1]
previous = sentences[i-1]
if DEBUG: print Tcolors.ACT + " Analyzing subjectivity..."
result = self.bootstrapping.classify(sentence, previous, next)
if result is None:
res = 'Not found!'
else:
res = result
if DEBUG:
print Tcolors.RES + Tcolors.OKGREEN + " " + res + Tcolors.ENDC
print
# If sentence is subjective
if result == 'subjective' or result is None:
# Proceed to polarity classification
if DEBUG: print Tcolors.ACT + " Analyzing sentiment..."
polarity_classifier = PolarityClassifier(self.pos_tagger, self.lexicon, debug=DEBUG)
sentiment, score, nscore = polarity_classifier.classify(sentence)
if DEBUG: print Tcolors.RES + Tcolors.OKGREEN + " " + sentiment + Tcolors.ENDC
# If sentence is objective
elif result == 'objective':
sentiment = 'neutral'
# Collect high-confidence training instances for SVM classifier.
# After the training, SVM can be used to classify new sentences.
#if sentiment != "neutral" and sentiment != "":
#if sentiment != "neutral" and abs(nscore) >= 0.4:
# self.total_sentences.append(sentence)
# self.total_sentiments.append(sentiment)
# Store results to memory
sentiments.append(sentiment)
scores.append(score)
nscores.append(nscore)
# Update score
if results.has_key(sentiment):
results[sentiment]['nscore'] += nscore
results[sentiment]['score'] += score
results[sentiment]['count'] += 1
print
print Tcolors.ACT + " Overall sentiment analysis:"
print Tcolors.BGH
print " Parts: ", len(sentences)
print " Sentiments: ", sentiments
print " Scores: ", scores
print " Results: ", "},\n\t ".join((str)(results).split("}, "))
print Tcolors.C
pcount = results['positive']['count']
ncount = results['negative']['count']
total = len(sentences)
print Tcolors.BG
print " subjective".ljust(16,"-") + "> %.2f" % ((float)(pcount + ncount)*100 / total) + "%"
print " objective".ljust(16,"-") + "> %.2f" % (100 - ((float)(pcount + ncount)*100 / total)) + "%"
print Tcolors.C
print Tcolors.BGGRAY
for sense in results.keys():
count = results[sense]['count']
percentage = (float)(count) * 100 / (len(sentences))
print " " +sense.ljust(15,"-")+"> %.2f" % (percentage) + "%"
print Tcolors.C
ssum = sum(scores)
confidence = " (%.2f, %.2f)" % (ssum,sum(nscores))
final_sent = ""
pos = True
if results["negative"]["count"] > len(sentences)*1.0/3:
pos = False
# Print total sentiment score and normalized sentiment score
if ssum > 0 and pos:
print Tcolors.RES + Tcolors.OKGREEN + " positive" + confidence + Tcolors.C
final_sent = "positive"
elif ssum == 0:
print Tcolors.RES + Tcolors.OKGREEN + " neutral" + confidence + Tcolors.C
final_sent = "neutral"
else:
print Tcolors.RES + Tcolors.OKGREEN + " negative" + confidence + Tcolors.C
final_sent = "negative"
print Tcolors.C
# Store results
total_result_hash = {'sentences' : sentences,
'sentiments': sentiments,
'scores' : scores,
'nscores' : nscores,
'results' : results,
'final' : {final_sent:{'score':ssum,'nscore':sum(nscores)}}}
# Train SVM classifier
# self.train_svm()
return total_result_hash
def normalize(self, text):
"""
Make some word improvements before feeding to the sentence tokenizer.
"""
rr = RepeatReplacer(self.lexicon)
normalized_text = []
final = None
try:
for word in text.split():
normal = rr.replace(word.lower())
if word[0].isupper():
normal = normal[0].upper() + normal[1:]
normalized_text.append(normal)
final = " ".join(normalized_text)
except:
final = text
return final
def train_svm(self):
"""
Train SVM and store data with pickle.
"""
self.svm.train(self.total_sentences, self.total_sentiments)
t_output = open(self.svm_train_filename,'wb')
l_output = open(self.svm_label_filename,'wb')
pickle.dump(self.total_sentences,t_output)
pickle.dump(self.total_sentiments,l_output)
t_output.close()
l_output.close()
pp=Probcpredict(q,mu_pos,mu_neg,sigma2_pos,sigma2_neg, z)
nn=Nearestneighbor(X,y,z)
print("Predict result of probcpredict:",pp.probcpredict())
print("Predict result of nearestneighbor:",nn.nearestneighbor())
#case 4_1
np.set_printoptions(precision=4)
X = np.array([[-3, 2],
[-2, 1.5],
[-1, 1],
[0, 0.5],
[1, 0],
[2, 2],
[-0.5, -1],
[0.5, 0]])
y = np.array([[1], [-1], [1], [-1], [1], [-1], [1], [-1]])
kf=Kfoldcv(2,X,y)
print("When k equals to 2, the accuracy of kfold is:",kf.kfoldcv())
bs=Bootstrapping(5,X,y)
np.random.seed(26)
print("When B equals to 5, the accuracy of bootstrapping is:",bs.bootstrapping())
# for Hypotest
a = np.array([[0.09],[0.08],[0.15],[0.11],[0.13]])
b = np.array([[0.10],[0.12],[0.14],[0.13],[0.13]])
ht=Hypotest(a,b,0.05)
print("When alpha equals to 0.05, the result of hypotest is:",ht.hypotest())
class Sentiment:
"""
Sentiment: Analyses the global sentiment of given text regions
that are decomposed to sentences, using bootstrapping methods for
subjectivity and polarity classification. All sub modules except
from POS tagging are learning by experience.
"""
def __init__(self):
self.pos_tagger = SequentialTagger()
self.hp_obj = HpObj(debug=DEBUG)
self.hp_subj = HpSubj(debug=DEBUG)
self.lexicon = self.hp_obj.lexicon
self.bootstrapping = Bootstrapping(self.hp_obj, self.hp_subj, self.pos_tagger, debug=DEBUG)
self.sentence_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
self.total_sentences = ["good","bad"]
self.total_sentiments = ["positive","negative"]
def analyze(self, clean_text_areas):
"""
Analysis of text regions using the following order: Each sentence per
region is passed from the subjectivity classification using bootstrapping
method and then if it turns out to be subjective it is passed
from the polarity classification using bootstrapping method also.
Finally, it results to a decision for the sentiment of the sentence
and the overall sentiment of the regions.
"""
if len(clean_text_areas) > 0:
for clean_text in clean_text_areas:
# Sentence detection
clean_text = self.normalize(clean_text)
try:
sentences = self.sentence_tokenizer.tokenize(clean_text)
except:
return {}
sentiments = []
scores = []
nscores = []
results = {'positive':{'count' : 0, 'score' : 0, 'nscore' : 0},
'neutral':{'count' : 0, 'score' : 0, 'nscore' : 0},
'negative':{'count' : 0, 'score' : 0, 'nscore' : 0}}
print
print Tcolors.ACT + " Checking block of text:"
for i, sentence in enumerate(sentences):
print "[" + str(i+1) + "] " + sentence
for i, sentence in enumerate(sentences):
# Proceed to subjectivity classification (bootstrapping procedure).
# (This step could be skipped in case you deal with subjective sentences only.)
sentiment = ""
previous = ""
next = ""
score = 0
nscore = 0
if i == 0 and i + 1 < len(sentences):
next = sentences[i+1]
elif i != 0 and i < len(sentences):
if i + 1 != len(sentences):
next = sentences[i+1]
previous = sentences[i-1]
if DEBUG: print Tcolors.ACT + " Analyzing subjectivity..."
result = self.bootstrapping.classify(sentence, previous, next)
if result is None:
res = 'Not found!'
else:
res = result
if DEBUG:
print Tcolors.RES + Tcolors.OKGREEN + " " + res + Tcolors.ENDC
print
# If sentence is subjective
if result == 'subjective' or result is None:
# Proceed to polarity classification
if DEBUG: print Tcolors.ACT + " Analyzing sentiment..."
polarity_classifier = PolarityClassifier(self.pos_tagger, self.lexicon, debug=DEBUG)
sentiment, score, nscore = polarity_classifier.classify(sentence)
if DEBUG: print Tcolors.RES + Tcolors.OKGREEN + " " + sentiment + Tcolors.ENDC
# If sentence is objective
elif result == 'objective':
sentiment = 'neutral'
# Collect high-confidence training instances for SVM classifier.
# After the training, SVM can be used to classify new sentences.
#if sentiment != "neutral" and sentiment != "":
#if sentiment != "neutral" and abs(nscore) >= 0.4:
# self.total_sentences.append(sentence)
# self.total_sentiments.append(sentiment)
# Store results to memory
sentiments.append(sentiment)
scores.append(score)
nscores.append(nscore)
# Update score
if results.has_key(sentiment):
results[sentiment]['nscore'] += nscore
results[sentiment]['score'] += score
results[sentiment]['count'] += 1
print
print Tcolors.ACT + " Overall sentiment analysis:"
print Tcolors.BGH
print " Parts: ", len(sentences)
print " Sentiments: ", sentiments
print " Scores: ", scores
print " Results: ", "},\n\t ".join((str)(results).split("}, "))
print Tcolors.C
pcount = results['positive']['count']
ncount = results['negative']['count']
total = len(sentences)
print Tcolors.BG
print " subjective".ljust(16,"-") + "> %.2f" % ((float)(pcount + ncount)*100 / total) + "%"
print " objective".ljust(16,"-") + "> %.2f" % (100 - ((float)(pcount + ncount)*100 / total)) + "%"
print Tcolors.C
print Tcolors.BGGRAY
for sense in results.keys():
count = results[sense]['count']
percentage = (float)(count) * 100 / (len(sentences))
print " " +sense.ljust(15,"-")+"> %.2f" % (percentage) + "%"
print Tcolors.C
ssum = sum(scores)
confidence = " (%.2f, %.2f)" % (ssum,sum(nscores))
final_sent = ""
pos = True
if results["negative"]["count"] > len(sentences)*1.0/3:
pos = False
# Print total sentiment score and normalized sentiment score
if ssum > 0 and pos:
print Tcolors.RES + Tcolors.OKGREEN + " positive" + confidence + Tcolors.C
final_sent = "positive"
elif ssum == 0:
print Tcolors.RES + Tcolors.OKGREEN + " neutral" + confidence + Tcolors.C
final_sent = "neutral"
else:
print Tcolors.RES + Tcolors.OKGREEN + " negative" + confidence + Tcolors.C
final_sent = "negative"
print Tcolors.C
# Store results
total_result_hash = {'sentences' : sentences,
'sentiments': sentiments,
'scores' : scores,
'nscores' : nscores,
'results' : results,
'final' : {final_sent:{'score':ssum,'nscore':sum(nscores)}}}
# Train SVM classifier
# self.train_svm()
return total_result_hash
def normalize(self, text):
"""
Make some word improvements before feeding to the sentence tokenizer.
"""
rr = RepeatReplacer(self.lexicon)
normalized_text = []
final = None
try:
for word in text.split():
normal = rr.replace(word.lower())
if word[0].isupper():
normal = normal[0].upper() + normal[1:]
normalized_text.append(normal)
final = " ".join(normalized_text)
except:
final = text
return final
def train_svm(self):
"""
Train SVM and store data with pickle.
"""
self.svm.train(self.total_sentences, self.total_sentiments)
t_output = open(self.svm_train_filename,'wb')
l_output = open(self.svm_label_filename,'wb')
pickle.dump(self.total_sentences,t_output)
pickle.dump(self.total_sentiments,l_output)
t_output.close()
l_output.close()
