-
Notifications
You must be signed in to change notification settings - Fork 0
/
text_clean.py
76 lines (65 loc) · 4.01 KB
/
text_clean.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
from nltk import WordNetLemmatizer, pos_tag, word_tokenize
wnl = WordNetLemmatizer()
ENGLISH_STOP_WORDS = frozenset([
"a", "about", "above", "across", "after", "afterwards", "again", "against",
"all", "almost", "alone", "along", "already", "also", "although", "always",
"am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
"any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
"around", "as", "at", "back", "be", "became", "because", "become",
"becomes", "becoming", "been", "before", "beforehand", "behind", "being",
"below", "beside", "besides", "between", "beyond", "bill", "both",
"bottom", "but", "by", "call", "can", "cannot", "cant", "co", "con",
"could", "couldnt", "cry", "de", "describe", "detail", "do", "done",
"down", "due", "during", "each", "eg", "eight", "either", "eleven", "else",
"elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone",
"everything", "everywhere", "except", "few", "fifteen", "fifty", "fill",
"find", "fire", "first", "five", "for", "former", "formerly", "forty",
"found", "four", "from", "front", "full", "further", "get", "give", "go",
"had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter",
"hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his",
"how", "however", "hundred", "i", "ie", "if", "in", "inc", "indeed",
"interest", "into", "is", "it", "its", "itself", "keep", "last", "latter",
"latterly", "least", "less", "ltd", "made", "many", "may", "me",
"meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly",
"move", "much", "must", "my", "myself", "name", "namely", "neither",
"never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone",
"nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on",
"once", "one", "only", "onto", "or", "other", "others", "otherwise", "our",
"ours", "ourselves", "out", "over", "own", "part", "per", "perhaps",
"please", "put", "rather", "re", "s", "same", "see", "seem", "seemed",
"seeming", "seems", "serious", "several", "she", "should", "show", "side",
"since", "sincere", "six", "sixty", "so", "some", "somehow", "someone",
"something", "sometime", "sometimes", "somewhere", "still", "such",
"system", "take", "ten", "than", "that", "the", "their", "them",
"themselves", "then", "thence", "there", "thereafter", "thereby",
"therefore", "therein", "thereupon", "these", "they", "thick", "thin",
"third", "this", "those", "though", "three", "through", "throughout",
"thru", "thus", "to", "together", "too", "top", "toward", "towards",
"twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us",
"very", "via", "was", "we", "well", "were", "what", "whatever", "when",
"whence", "whenever", "where", "whereafter", "whereas", "whereby",
"wherein", "whereupon", "wherever", "whether", "which", "while", "whither",
"who", "whoever", "whole", "whom", "whose", "why", "will", "with",
"within", "without", "would", "yet", "you", "your", "yours", "yourself",
"yourselves"])
def clean_digits(df):
df['text'] = df['text'].str.replace('\d+', '') # for digits
df['text'] = df['text'].str.replace(r'(\b\w{1,2}\b)', '') # for words
df['text'] = df['text'].str.replace('[^\w\s]', '') # for punctuation
def preprocess_text(text):
# Input: str, i.e. document/sentence
# Output: list(str) , i.e. list of lemmas
return [word for word in lemmatize_sent(text)
if word not in ENGLISH_STOP_WORDS and word.isalnum() and word not in ['rt', 'url', 'l', 't']]
def lemmatize_sent(text):
# Text input is string, returns lowercased strings.
return [wnl.lemmatize(word.lower(), pos=penn2morphy(tag))
for word, tag in pos_tag(word_tokenize(text))]
def penn2morphy(penntag):
""" Converts Penn Treebank tags to WordNet. """
morphy_tag = {'NN': 'n', 'JJ': 'a',
'VB': 'v', 'RB': 'r'}
try:
return morphy_tag[penntag[:2]]
except:
return 'n'