-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathTweetModel.py
159 lines (144 loc) · 7.19 KB
/
TweetModel.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
158
159
import nltk
import pickle
nltk.download('punkt')
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from math import log, sqrt
class TweetClassifier(object):
def __init__(self, trainData, method='tf-idf'):
self.tweets, self.labels = trainData['message'], trainData['label']
self.method = method
def train(self):
self.calc_TF_and_IDF()
if self.method == 'tf-idf':
self.calc_TF_IDF()
else:
self.calc_prob()
def calc_prob(self):
self.prob_depressive = dict()
self.prob_positive = dict()
for word in self.tf_depressive:
self.prob_depressive[word] = (self.tf_depressive[word] + 1) / (self.depressive_words + \
len(list(self.tf_depressive.keys())))
for word in self.tf_positive:
self.prob_positive[word] = (self.tf_positive[word] + 1) / (self.positive_words + \
len(list(self.tf_positive.keys())))
self.prob_depressive_tweet, self.prob_positive_tweet = self.depressive_tweets / self.total_tweets, self.positive_tweets / self.total_tweets
def calc_TF_and_IDF(self):
noOfMessages = self.tweets.shape[0]
self.depressive_tweets, self.positive_tweets = self.labels.value_counts()[1], self.labels.value_counts()[0]
self.total_tweets = self.depressive_tweets + self.positive_tweets
self.depressive_words = 0
self.positive_words = 0
self.tf_depressive = dict()
self.tf_positive = dict()
self.idf_depressive = dict()
self.idf_positive = dict()
for i in range(noOfMessages):
message_processed = process_message(self.tweets.iloc[i])
count = list() # To keep track of whether the word has ocured in the message or not.
# For IDF
for word in message_processed:
if self.labels.iloc[i]:
self.tf_depressive[word] = self.tf_depressive.get(word, 0) + 1
self.depressive_words += 1
else:
self.tf_positive[word] = self.tf_positive.get(word, 0) + 1
self.positive_words += 1
if word not in count:
count += [word]
for word in count:
if self.labels.iloc[i]:
self.idf_depressive[word] = self.idf_depressive.get(word, 0) + 1
else:
self.idf_positive[word] = self.idf_positive.get(word, 0) + 1
pickle_out = open("dataB.pickle","wb")
pickle.dump(self.depressive_words,pickle_out)
pickle.dump(self.positive_words,pickle_out)
pickle_out.close()
def calc_TF_IDF(self):
self.prob_depressive = dict()
self.prob_positive = dict()
self.sum_tf_idf_depressive = 0
self.sum_tf_idf_positive = 0
for word in self.tf_depressive:
self.prob_depressive[word] = (self.tf_depressive[word]) * log(
(self.depressive_tweets + self.positive_tweets) \
/ (self.idf_depressive[word] + self.idf_positive.get(word, 0)))
self.sum_tf_idf_depressive += self.prob_depressive[word]
for word in self.tf_depressive:
self.prob_depressive[word] = (self.prob_depressive[word] + 1) / (
self.sum_tf_idf_depressive + len(list(self.prob_depressive.keys())))
for word in self.tf_positive:
self.prob_positive[word] = (self.tf_positive[word]) * log((self.depressive_tweets + self.positive_tweets) \
/ (self.idf_depressive.get(word, 0) +
self.idf_positive[word]))
self.sum_tf_idf_positive += self.prob_positive[word]
for word in self.tf_positive:
self.prob_positive[word] = (self.prob_positive[word] + 1) / (
self.sum_tf_idf_positive + len(list(self.prob_positive.keys())))
self.prob_depressive_tweet, self.prob_positive_tweet = self.depressive_tweets / self.total_tweets, self.positive_tweets / self.total_tweets
pickle_out = open("dataA.pickle","wb")
pickle.dump(self.prob_depressive,pickle_out)
pickle.dump(self.sum_tf_idf_depressive,pickle_out)
pickle.dump(self.prob_positive,pickle_out)
pickle.dump(self.sum_tf_idf_positive,pickle_out)
pickle.dump(self.prob_depressive_tweet,pickle_out)
pickle.dump(self.prob_positive_tweet,pickle_out)
pickle_out.close()
def classify(self, processed_message,method):
pickle_in = open("dataA.pickle","rb")
prob_depressive = pickle.load(pickle_in)
sum_tf_idf_depressive = pickle.load(pickle_in)
prob_positive = pickle.load(pickle_in)
sum_tf_idf_positive = pickle.load(pickle_in)
prob_depressive_tweet = pickle.load(pickle_in)
prob_positive_tweet = pickle.load(pickle_in)
pickle_in = open("dataB.pickle","rb")
depressive_words = pickle.load(pickle_in)
positive_words = pickle.load(pickle_in)
pDepressive, pPositive = 0, 0.
for word in processed_message:
if word in prob_depressive:
pDepressive += log(prob_depressive[word])
else:
if method == 'tf-idf':
pDepressive -= log(sum_tf_idf_depressive + len(list(prob_depressive.keys())))
else:
pDepressive -= log(depressive_words + len(list(prob_depressive.keys())))
if word in prob_positive:
pPositive += log(prob_positive[word])
else:
if method == 'tf-idf':
pPositive -= log(sum_tf_idf_positive + len(list(prob_positive.keys())))
else:
pPositive -= log(positive_words + len(list(prob_positive.keys())))
pDepressive += log(prob_depressive_tweet)
pPositive += log(prob_positive_tweet)
return pDepressive >= pPositive
def predict(self, testData,method):
result = dict()
for (i, message) in enumerate(testData):
processed_message = process_message(message)
result[i] = int(self.classify(processed_message,method))
return result
def process_message(message, lower_case = True, stem = True, stop_words = True, gram = 2):
if lower_case:
message = message.lower()
words = word_tokenize(message)
words = [w for w in words if len(w) > 2]
if gram > 1:
w = []
for i in range(len(words) - gram + 1):
w += [' '.join(words[i:i + gram])]
return w
if stop_words:
sw = stopwords.words('english')
words = [word for word in words if word not in sw]
if stem:
stemmer = PorterStemmer()
words = [stemmer.stem(word) for word in words]
return words