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700d42b | 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 | import re
import string
class TextProcessor:
"""Handles normalization, cleaning, and phrase detection."""
STOPWORDS = {
'i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd",
'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers',
'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which',
'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been',
'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if',
'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between',
'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out',
'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why',
'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not',
'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should',
"should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't",
'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't",
'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't",
'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"
}
@staticmethod
def clean_html(text):
if not text: return ""
# Remove HTML tags
clean = re.sub(r'<[^>]+>', ' ', text)
# Remove extra whitespace
clean = re.sub(r'\s+', ' ', clean).strip()
return clean
@classmethod
def normalize(cls, text):
if not text: return ""
# Lowercase
text = text.lower()
# Remove punctuation
text = text.translate(str.maketrans('', '', string.punctuation))
# Remove stopwords
tokens = [t for t in text.split() if t not in cls.STOPWORDS]
return " ".join(tokens)
@staticmethod
def detect_phrases(text):
"""Simple bigram detection for important PPD concepts."""
phrases = [
"postpartum depression", "maternal mental health", "sleep disturbance",
"crying spells", "suicidal ideation", "mood swings", "baby blues"
]
for p in phrases:
# We don't replace, we just ensure they are treated as one token for TF-IDF if we want,
# but scikit-learn's ngram_range can also do this.
# To force it, we could underscore them:
if p in text.lower():
underscored = p.replace(" ", "_")
text = re.sub(p, underscored, text, flags=re.IGNORECASE)
return text
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