-
Notifications
You must be signed in to change notification settings - Fork 8
/
Cleaning Tweets.py
142 lines (107 loc) · 3.55 KB
/
Cleaning Tweets.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
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import pandas as pd
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
import re
import string
import random
from nltk.tokenize import WordPunctTokenizer
from nltk.tag import pos_tag
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus im pplport stopwords
# In[ ]:
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
# In[ ]:
from google.colab import files
uploaded = files.upload()
# In[ ]:
def extract_csv():
my_filtered_csv = pd.read_csv('clown_1.csv', usecols=['tweet'])
return my_filtered_csv
def tokenize_tweets(clown_1) :
tweets = clown_1.tweet.tolist()
tokenizer = WordPunctTokenizer()
cleaned = []
for i in range(0, len(tweets)):
text = tweets[i]
text = re.sub('^https?://.*[rn]*','', text)
text = re.sub(r'\w+:\/{2}[\d\w-]+(\.[\d\w-]+)*(?:(?:\/[^\s/]*))*', '', text)
text = re.sub("(@[A-Za-z0-9_]+)","", text)
text = re.sub("([^\w\s])", "", text)
text = re.sub("^RT", "", text)
text = tokenizer.tokenize(text)
element = [text]
cleaned.append(element)
return cleaned
def lemmatize_sentence(tweet_tokens, stop_words = ()):
lemmatizer = WordNetLemmatizer()
cleaned_tokens = []
for token, tag in pos_tag(tweet_tokens):
if tag.startswith('NN'):
pos = 'n'
elif tag.startswith('V'):
pos = 'v'
else:
pos = 'a'
token = lemmatizer.lemmatize(token, pos)
if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:
cleaned_tokens.append(token.lower())
return cleaned_tokens
def create_lemmatized_sent(words):
cleaned = []
stop_words = stopwords.words('english')
for i in range(0, len(words)):
sent = lemmatize_sentence(words[i][0], stop_words)
if len(sent) >= 0:
element = [sent]
cleaned.append(element)
return cleaned
def remove_emoji(string):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
"]+", flags=re.UNICODE)
return emoji_pattern.sub(r'', string)
def write_sent(sent):
cleaned = []
for i in sent:
s = ""
for j in i[0]:
j = str(j)
j = j + " "
s = s + j
s = remove_emoji(s)
element = [s]
cleaned.append(element)
df = pd.DataFrame(cleaned)
# print(df.iloc[1])
#df.to_csv('cleaned_clown_1.csv', index=False)
df1 = pd.read_csv('clown_1.csv')
df1 = df1['time']
big = pd.concat([df, df1], axis = 1)
big.to_csv('cleaned_clown_1.csv', index=False)
clown_1 = extract_csv()
words = tokenize_tweets(clown_1)
sent = create_lemmatized_sent(words)
write_sent(sent)
# In[ ]:
df = pd.read_csv('cleaned_clown_1.csv')
df = df.rename(columns={"0": "clean_tweet"})
df
# In[ ]:
df.to_csv('cleaned_clown_1.csv', index=True)
files.download('cleaned_clown_1.csv')
# In[ ]: