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Advanced preprocessing pipeline.
Adds: contraction expansion, negation tagging, language detection, Flesch score.
"""
import re
import unicodedata
import pandas as pd
import spacy
# ── Contraction map ────────────────────────────────────────────────────────────
CONTRACTIONS = {
"won't": "will not", "can't": "cannot", "couldn't": "could not",
"wouldn't": "would not", "shouldn't": "should not", "isn't": "is not",
"aren't": "are not", "wasn't": "was not", "weren't": "were not",
"don't": "do not", "doesn't": "does not", "didn't": "did not",
"haven't": "have not", "hasn't": "has not", "hadn't": "had not",
"I'm": "I am", "I've": "I have", "I'll": "I will", "I'd": "I would",
"you're": "you are", "you've": "you have", "you'll": "you will",
"he's": "he is", "she's": "she is", "it's": "it is",
"we're": "we are", "we've": "we have", "we'll": "we will",
"they're": "they are", "they've": "they have", "they'll": "they will",
"that's": "that is", "there's": "there is", "here's": "here is",
"let's": "let us", "who's": "who is", "what's": "what is",
"n't": " not",
}
_CONTRACTION_RE = re.compile(
r'\b(' + '|'.join(re.escape(k) for k in sorted(CONTRACTIONS, key=len, reverse=True)) + r')\b',
re.IGNORECASE
)
def expand_contractions(text: str) -> str:
def _replace(match):
token = match.group(0)
return CONTRACTIONS.get(token, CONTRACTIONS.get(token.lower(), token))
return _CONTRACTION_RE.sub(_replace, text)
# ── Negation tagger ────────────────────────────────────────────────────────────
_NEGATION_WORDS = {"not", "no", "never", "neither", "nobody", "nothing",
"nowhere", "nor", "cannot", "n't"}
_CLAUSE_PUNCT = re.compile(r'[,;:.!?]')
def tag_negations(tokens: list[str]) -> list[str]:
"""
Prefix each token in a negation scope with NOT_.
Scope ends at clause-boundary punctuation or after 5 tokens.
"""
result = []
negating = False
neg_count = 0
for tok in tokens:
if tok.lower() in _NEGATION_WORDS:
negating = True
neg_count = 0
result.append(tok)
continue
if negating:
if _CLAUSE_PUNCT.search(tok) or neg_count >= 5:
negating = False
neg_count = 0
result.append(tok)
else:
result.append(f"NOT_{tok}")
neg_count += 1
else:
result.append(tok)
return result
# ── Language detection (lightweight heuristic) ─────────────────────────────────
_COMMON_ENGLISH = {
"the", "be", "to", "of", "and", "a", "in", "that", "have", "it",
"for", "not", "on", "with", "he", "as", "you", "do", "at", "this",
"but", "his", "by", "from", "they", "we", "say", "her", "she", "or",
"an", "will", "my", "one", "all", "would", "there", "their", "what",
"so", "up", "out", "if", "about", "who", "get", "which", "go", "me"
}
def is_english(text: str, threshold: float = 0.15) -> bool:
"""Simple token-overlap heuristic. Returns True if likely English."""
tokens = re.findall(r'\b[a-z]+\b', text.lower())
if not tokens:
return False
overlap = sum(1 for t in tokens if t in _COMMON_ENGLISH)
return (overlap / len(tokens)) >= threshold
# ── Flesch Reading Ease ────────────────────────────────────────────────────────
def count_syllables(word: str) -> int:
"""Approximate syllable count using vowel-group heuristic."""
word = word.lower().strip(".,!?;:")
if len(word) <= 3:
return 1
vowels = re.findall(r'[aeiouy]+', word)
count = len(vowels)
if word.endswith('e'):
count -= 1
return max(1, count)
def flesch_reading_ease(text: str) -> float:
"""
Flesch Reading Ease score.
206.835 − 1.015*(words/sentences) − 84.6*(syllables/words)
Higher = easier to read. Typical range: 0–100.
"""
sentences = max(1, len(re.split(r'[.!?]+', text)))
words = re.findall(r'\b\w+\b', text)
if not words:
return 0.0
syllables = sum(count_syllables(w) for w in words)
asl = len(words) / sentences # avg sentence length
asw = syllables / len(words) # avg syllables per word
score = 206.835 - 1.015 * asl - 84.6 * asw
return round(max(0.0, min(100.0, score)), 2)
# ── Full pipeline ──────────────────────────────────────────────────────────────
def clean_advanced(text: str, nlp=None) -> dict:
"""
Run the full advanced cleaning pipeline on a single text.
Returns a dict with cleaned text variants and the Flesch score.
"""
# 1. Unicode normalise
text = unicodedata.normalize("NFKC", text)
# 2. Strip HTML/URLs
text = re.sub(r'<[^>]+>', ' ', text)
text = re.sub(r'http\S+|www\.\S+', ' ', text)
# 3. Expand contractions
expanded = expand_contractions(text)
# 4. Language check
if not is_english(expanded):
return None # caller should drop row
# 5. Flesch score on original (before lemmatization)
flesch = flesch_reading_ease(expanded)
# 6. Lowercase + tokenize for classical features
clean = re.sub(r'[^a-z\s]', ' ', expanded.lower())
tokens = clean.split()
# 7. Negation tagging
neg_tokens = tag_negations(tokens)
neg_text = " ".join(neg_tokens)
return {
"text_raw": text, # keep original case for BERT
"text_clean": expanded, # contraction-expanded for BERT input
"text_neg": neg_text, # negation-tagged for TF-IDF / NRC
"flesch": flesch,
"is_english": True,
}
def clean_dataframe(df: pd.DataFrame, text_col: str = "text",
target_col: str = "extraversion") -> pd.DataFrame:
"""Apply clean_advanced to every row, drop non-English rows."""
records = []
for _, row in df.iterrows():
result = clean_advanced(str(row[text_col]))
if result is None:
continue
result[target_col] = row[target_col]
records.append(result)
return pd.DataFrame(records)
if __name__ == "__main__":
sample = "I don't like big parties, they're too loud and I can't focus."
out = clean_advanced(sample)
print("Raw: ", out["text_raw"])
print("Expanded: ", out["text_clean"])
print("Negation: ", out["text_neg"])
print("Flesch Score: ", out["flesch"])
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