-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathSentimentalAnalysis-NN.py
157 lines (135 loc) · 6.52 KB
/
SentimentalAnalysis-NN.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
import pandas as pd
import re
import math
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_curve,auc
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.callbacks import ModelCheckpoint
from keras.wrappers.scikit_learn import KerasClassifier
#importing dataset
dataset=pd.read_csv('I://3//Projects//Business//Sentiment Analysis-python//Restaurant_reviews.tsv',delimiter='\t',quoting=3)
#Data preprocessing phase
corpus = []
for i in range(0, 1000):
review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
corpus.append(review)
cv = CountVectorizer(max_features = 1500)
x = cv.fit_transform(corpus).toarray()
y = dataset.iloc[:,1].values
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
#training neural networks
nn_classifier=Sequential()
nn_classifier.add(Dense(input_dim=1500,units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))
nn_classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
path='I://3//Projects//Business//Sentiment Analysis-python//sent_weights//weights.{epoch:02d}-{loss:.2f}.hdf5'
mcp=ModelCheckpoint(path,monitor='loss',save_best_only=True,verbose=0)
nn_classifier.fit(x_train,y_train,epochs=100,batch_size=30,callbacks=[mcp])
sent_pred=nn_classifier.predict(x_test)
test_set=(sent_pred>0.5)
nn_cm=confusion_matrix(y_test,test_set)
nn_accuracy=(nn_cm[0,0]+nn_cm[1,1])/(nn_cm[0,0]+nn_cm[1,1]+nn_cm[0,1]+nn_cm[1,0])*100
nn_precision=(nn_cm[0,0])/(nn_cm[0,0]+nn_cm[0,1])
nn_recall=(nn_cm[0,0])/(nn_cm[0,0]+nn_cm[1,0])
nn_f1_score=(2*nn_precision*nn_recall)/(nn_precision+nn_recall)
nn_fpr,nn_tpr,nn_threshold=roc_curve(test_set,y_test)
nn_roc_auc = auc(nn_fpr,nn_tpr)
print("Accuracy of NN is {}%".format(math.floor(nn_accuracy)))
print("Precision of NN is {}%".format(math.floor(nn_precision*100)))
print("Recall of NN is {}%".format(math.floor(nn_recall*100)))
print("F1_score of NN is {}%".format(math.floor(nn_f1_score*100)))
print("ROC_curve of NN is {}%".format(math.floor(nn_roc_auc*100)))
#nb_roc_curve graph
plt.title('NN Receiver Operating Characteristic')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.plot(nn_fpr,nn_tpr)
plt.plot([0, 1], [0, 1],'r--')
plt.savefig('I://3//Projects//Business//Sentiment Analysis-python//NN.png')
#cross validation data
def build_classifier():
nn_classifier=Sequential()
nn_classifier.add(Dense(input_dim=1500,units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))
nn_classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
return nn_classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)
accuracies = cross_val_score(estimator = classifier, X = x_train, y = y_train, cv = 10, n_jobs = -1)
mean = accuracies.mean()
variance = accuracies.std()
#grid search cv
def build_classifier(optimizer):
nn_classifier=Sequential()
nn_classifier.add(Dense(input_dim=1500,units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))
nn_classifier.compile(optimizer=optimizer,loss='binary_crossentropy',metrics=['accuracy'])
return nn_classifier
classifier = KerasClassifier(build_fn = build_classifier)
parameters={'optimizer':['adam','rmsprop'],'batch_size':[20,30,40],'epochs':[80,100,120]}
gscv=GridSearchCV(estimator=classifier,param_grid=parameters,cv=10,scoring='accuracy')
gscv_model=gscv.fit(x_train,y_train)
gscv.best_params_
gscv.best_score_
#Deploying model
nn_classifier=Sequential()
nn_classifier.add(Dense(input_dim=1500,units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=750,activation='relu',kernel_initializer='uniform'))
nn_classifier.add(Dropout(rate=0.2))
nn_classifier.add(Dense(units=1,activation='sigmoid',kernel_initializer='uniform'))
nn_classifier.load_weights("I://3//Projects//Business//Sentiment Analysis-python//weights.48-0.01.hdf5")
#data preprocessing of new predictions
def format_review(review):
review = re.sub('[^a-zA-Z]', ' ', review)
review = review.lower()
review = review.split()
ps = PorterStemmer()
review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
review = ' '.join(review)
return review
new_review ='fodd was awesome'
new_review = format_review(new_review)
test_corpus = []
test_corpus.append(new_review)
X_new_test = cv.transform(test_corpus).toarray()
#predicting new review
predicted_new=nn_classifier.predict(X_new_test)
if(predicted_new>0.5):
print("FOOD IS GOOD")
else:
print("FOOD IS BAD")