xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, data[1], linewidth=2, label=lb)
plt.title(t)
plt.legend(loc="upper right")
plt.xlabel("sentiment score")
plt.show()
#read labels and messages from dataset
dataset = "datasets/train15.tsv"
#dataset = "datasets/training-set-sample.tsv"
labels, messages = tsvreader.opentsv(dataset)
##labels = labels[0:100]
##messages = messages[0:100]
#pos tags of messages
tags = arktagger.pos_tag_list(messages)
#initialize lists that hold the sentiment score of every message for every Lexicon
socal_scores = []
minqinghu_scores = []
afinn_scores = []
nrc1_scores = []
nrc2_scores = []
nrc3_scores = []
nrc4_scores = []
def main(f):
print "System training started"
#load training dataset
dataset_train = f
ids,labels_train,messages_train=tsvreader.opentsv(dataset_train)
print "Train data loaded"
#labels for subjectivity detection (2 categories)
temp_labels_train = [0 if x=="neutral" else 1 for x in labels_train]
#labels for polarity detection (3 categories)
labels_train = [0 if x=="neutral" else -1 if x =="negative" else 1 for x in labels_train]
#convert labels to numpy arrays
temp_labels_train=np.array(temp_labels_train)
labels_train=np.array(labels_train)
#load word clusters
clusters = loadClusters()
print "Clusters loaded"
#load Lexicons
negationList, slangDictionary, lexicons, mpqa_lexicons = loadLexicons()
print "Lexicons loaded"
#tokenize all messages
tokens_train = tokenize(messages_train)
print "Messages tokenized"
#compute pos tags for all messages
pos_tags_train = arktagger.pos_tag_list(messages_train)
print "Pos tags computed"
#compute pos tag bigrams
pos_bigrams_train = getBigrams(pos_tags_train)
#compute pos tag trigrams
pos_trigrams_train = getTrigrams(pos_tags_train)
#get the unique pos bigrams from training set
unique_pos_tags = getPosTagsSet(pos_tags_train)
unique_bigrams = getBigramsSet(pos_bigrams_train)
unique_trigrams= getTrigramsSet(pos_trigrams_train)
#compute POS tag scores
pos_tags_scores_neutral = posTagsScore(unique_pos_tags,0,pos_tags_train,labels_train)
pos_tags_scores_positive = posTagsScore(unique_pos_tags,1,pos_tags_train,labels_train)
pos_tags_scores_negative = posTagsScore(unique_pos_tags,-1,pos_tags_train,labels_train)
pos_bigrams_scores_neutral = posBigramsScore(unique_bigrams,0,pos_bigrams_train,labels_train)
pos_bigrams_scores_positive = posBigramsScore(unique_bigrams,1,pos_bigrams_train,labels_train)
pos_bigrams_scores_negative = posBigramsScore(unique_bigrams,-1,pos_bigrams_train,labels_train)
pos_trigrams_scores_neutral = posTrigramsScore(unique_trigrams,0,pos_trigrams_train,labels_train)
pos_trigrams_scores_positive = posTrigramsScore(unique_trigrams,1,pos_trigrams_train,labels_train)
pos_trigrams_scores_negative = posTrigramsScore(unique_trigrams,-1,pos_trigrams_train,labels_train)
#compute mpqa scores
mpqaScores = getScores(mpqa_lexicons,messages_train,labels_train,neutral=True)
#save scores and other resources for future use
savePosScores(pos_tags_scores_neutral, pos_tags_scores_positive,pos_tags_scores_negative,pos_bigrams_scores_neutral,pos_bigrams_scores_positive,pos_bigrams_scores_negative,pos_trigrams_scores_neutral,pos_trigrams_scores_positive,pos_trigrams_scores_negative,mpqaScores)
#save lexicons
saveLexicons(negationList,slangDictionary,lexicons,mpqa_lexicons)
#save clusters
saveClusters(clusters)
#load Glove embeddings
d = 200
glove = GloveDictionary.Glove(d)
#save Glove embeddings for future use
saveGlove(glove)
#Subjectivity Detection Features
#SD1 features
features_train_1 = features.getFeatures(messages_train,tokens_train,pos_tags_train,slangDictionary,lexicons,mpqa_lexicons,pos_bigrams_train,pos_trigrams_train,pos_bigrams_scores_negative,pos_bigrams_scores_positive,pos_trigrams_scores_negative,pos_trigrams_scores_positive,pos_tags_scores_negative,pos_tags_scores_positive,mpqaScores,negationList,clusters,pos_bigrams_scores_neutral,pos_trigrams_scores_neutral,pos_tags_scores_neutral)
#SD2 features
features_train_2 = []
#for message in tokens_train :
for i in range(0,len(messages_train)):
features_train_2.append(glove.findCentroid(tokens_train[i]))
features_train_2 = np.array(features_train_2)
#regularize features
features_train_1 = regularization.regularize(features_train_1)
features_train_2 = regularization.regularizeHorizontally(features_train_2)
#Penalty parameter C of the error term for every SD system
C1=0.001953125
C2=1.4068830572470667
#get confidence scores
train_confidence_1 = getConfidenceScores(features_train_1, temp_labels_train, C1)
train_confidence_2 = getConfidenceScores(features_train_2, temp_labels_train, C2)
#normalize confidence scores
softmax = lambda x: 1 / (1. + math.exp(-x))
train_confidence_1 = [softmax(conf) for conf in train_confidence_1]
train_confidence_2 = [softmax(conf) for conf in train_confidence_2]
train_confidence_1 = np.array(train_confidence_1)
train_confidence_2 = np.array(train_confidence_2)
#train SD classifiers
sd1 = SVM.train(features_train_1,temp_labels_train,c=C1,k="linear")
sd2 = SVM.train(features_train_2,temp_labels_train,c=C2,k="linear")
#Sentiment Polarity Features (append confidence scores to SD features)
#SP1 features
features_train_1 = np.hstack((features_train_1,train_confidence_1.reshape(train_confidence_1.shape[0],1)))
#SP1 features
features_train_2 = np.hstack((features_train_2,train_confidence_2.reshape(train_confidence_2.shape[0],1)))
#Penalty parameter C of the error term for every SP system
C1=0.003410871889693192
C2=7.396183688299606
#train SP classifiers
sp1 = SVM.train(features_train_1,labels_train,c=C1,k="linear")
sp2 = SVM.train(features_train_2,labels_train,c=C2,k="linear")
#save trained models
saveModels(sd1,sd2,sp1,sp2)
print "System training completed!"
def main(f):
print "System training started"
#load training dataset
dataset_train = f
ids, labels_train, messages_train = tsvreader.opentsv(dataset_train)
print "Train data loaded"
#labels for subjectivity detection (2 categories)
temp_labels_train = [0 if x == "neutral" else 1 for x in labels_train]
#labels for polarity detection (3 categories)
labels_train = [
0 if x == "neutral" else -1 if x == "negative" else 1
for x in labels_train
]
#convert labels to numpy arrays
temp_labels_train = np.array(temp_labels_train)
labels_train = np.array(labels_train)
#load word clusters
clusters = loadClusters()
print "Clusters loaded"
#load Lexicons
negationList, slangDictionary, lexicons, mpqa_lexicons = loadLexicons()
print "Lexicons loaded"
#tokenize all messages
tokens_train = tokenize(messages_train)
print "Messages tokenized"
#compute pos tags for all messages
pos_tags_train = arktagger.pos_tag_list(messages_train)
print "Pos tags computed"
#compute pos tag bigrams
pos_bigrams_train = getBigrams(pos_tags_train)
#compute pos tag trigrams
pos_trigrams_train = getTrigrams(pos_tags_train)
#get the unique pos bigrams from training set
unique_pos_tags = getPosTagsSet(pos_tags_train)
unique_bigrams = getBigramsSet(pos_bigrams_train)
unique_trigrams = getTrigramsSet(pos_trigrams_train)
#compute POS tag scores
pos_tags_scores_neutral = posTagsScore(unique_pos_tags, 0, pos_tags_train,
labels_train)
pos_tags_scores_positive = posTagsScore(unique_pos_tags, 1, pos_tags_train,
labels_train)
pos_tags_scores_negative = posTagsScore(unique_pos_tags, -1,
pos_tags_train, labels_train)
pos_bigrams_scores_neutral = posBigramsScore(unique_bigrams, 0,
pos_bigrams_train,
labels_train)
pos_bigrams_scores_positive = posBigramsScore(unique_bigrams, 1,
pos_bigrams_train,
labels_train)
pos_bigrams_scores_negative = posBigramsScore(unique_bigrams, -1,
pos_bigrams_train,
labels_train)
pos_trigrams_scores_neutral = posTrigramsScore(unique_trigrams, 0,
pos_trigrams_train,
labels_train)
pos_trigrams_scores_positive = posTrigramsScore(unique_trigrams, 1,
pos_trigrams_train,
labels_train)
pos_trigrams_scores_negative = posTrigramsScore(unique_trigrams, -1,
pos_trigrams_train,
labels_train)
#compute mpqa scores
mpqaScores = getScores(mpqa_lexicons,
messages_train,
labels_train,
neutral=True)
#save scores and other resources for future use
savePosScores(pos_tags_scores_neutral, pos_tags_scores_positive,
pos_tags_scores_negative, pos_bigrams_scores_neutral,
pos_bigrams_scores_positive, pos_bigrams_scores_negative,
pos_trigrams_scores_neutral, pos_trigrams_scores_positive,
pos_trigrams_scores_negative, mpqaScores)
#save lexicons
saveLexicons(negationList, slangDictionary, lexicons, mpqa_lexicons)
#save clusters
saveClusters(clusters)
#load Glove embeddings
d = 200
glove = GloveDictionary.Glove(d)
#save Glove embeddings for future use
saveGlove(glove)
#Subjectivity Detection Features
#SD1 features
features_train_1 = features.getFeatures(
messages_train, tokens_train, pos_tags_train, slangDictionary,
lexicons, mpqa_lexicons, pos_bigrams_train, pos_trigrams_train,
pos_bigrams_scores_negative, pos_bigrams_scores_positive,
pos_trigrams_scores_negative, pos_trigrams_scores_positive,
pos_tags_scores_negative, pos_tags_scores_positive, mpqaScores,
negationList, clusters, pos_bigrams_scores_neutral,
pos_trigrams_scores_neutral, pos_tags_scores_neutral)
#SD2 features
features_train_2 = []
#for message in tokens_train :
for i in range(0, len(messages_train)):
features_train_2.append(glove.findCentroid(tokens_train[i]))
features_train_2 = np.array(features_train_2)
#regularize features
features_train_1 = regularization.regularize(features_train_1)
features_train_2 = regularization.regularizeHorizontally(features_train_2)
#Penalty parameter C of the error term for every SD system
C1 = 0.001953125
C2 = 1.4068830572470667
#get confidence scores
train_confidence_1 = getConfidenceScores(features_train_1,
temp_labels_train, C1)
train_confidence_2 = getConfidenceScores(features_train_2,
temp_labels_train, C2)
#normalize confidence scores
softmax = lambda x: 1 / (1. + math.exp(-x))
train_confidence_1 = [softmax(conf) for conf in train_confidence_1]
train_confidence_2 = [softmax(conf) for conf in train_confidence_2]
train_confidence_1 = np.array(train_confidence_1)
train_confidence_2 = np.array(train_confidence_2)
#train SD classifiers
sd1 = SVM.train(features_train_1, temp_labels_train, c=C1, k="linear")
sd2 = SVM.train(features_train_2, temp_labels_train, c=C2, k="linear")
#Sentiment Polarity Features (append confidence scores to SD features)
#SP1 features
features_train_1 = np.hstack(
(features_train_1,
train_confidence_1.reshape(train_confidence_1.shape[0], 1)))
#SP1 features
features_train_2 = np.hstack(
(features_train_2,
train_confidence_2.reshape(train_confidence_2.shape[0], 1)))
#Penalty parameter C of the error term for every SP system
C1 = 0.003410871889693192
C2 = 7.396183688299606
#train SP classifiers
sp1 = SVM.train(features_train_1, labels_train, c=C1, k="linear")
sp2 = SVM.train(features_train_2, labels_train, c=C2, k="linear")
#save trained models
saveModels(sd1, sd2, sp1, sp2)
print "System training completed!"
import learningCurves
#load the data
dataset_train = "datasets/train15.tsv"
dataset_test = "datasets/dev15.tsv"
#dataset_train = "datasets/full_train.tsv"
#dataset_test = "datasets/full_dev.tsv"
#dataset_train = "datasets/training-set-sample.tsv"
#dataset_test = "datasets/testing-set-sample.tsv"
subjectivity=True
if subjectivity:
labels_train,messages_train=tsvreader.opentsv(dataset_train)
labels_test, messages_test =tsvreader.opentsv(dataset_test)
labels_train = [0 if x=="neutral" else 1 for x in labels_train]
labels_test = [0 if x=="neutral" else 1 for x in labels_test]
else:
labels_train,messages_train=tsvreader.opentsvPolarity(dataset_train)
labels_test, messages_test =tsvreader.opentsvPolarity(dataset_test)
labels_train = [0 if x=="negative" else 1 for x in labels_train]
labels_test = [0 if x=="negative" else 1 for x in labels_test]
#tokenize all messages
tokens_train = tokenize(messages_train)
tokens_test = tokenize(messages_test)
