52. Text Cleaning and TF-IDF in Python
52.3 Hinglish and Messy Text Tips
Why. Hinglish and typo-heavy WhatsApp text breaks naive English stop-word lists. Keep expectations honest for India data.
What. Classical TF-IDF works best when most tokens are consistent. Mixed Marathi–English, emoji, and transliteration ("dhahi"/"dahi") add noise. You can still learn useful baselines; perfect language ID is out of scope here.
How.
Practical India tips
- Normalise a few known spellings in a small replace map if needed.
- Do not expect English
stop_wordsto clean Marathi tokens. - Start with a labelled English/Hinglish practice set; expand later.
- For heavy multilingual production, plan a Gen AI / specialised path later.
fixes = {"dhahi": "dahi", "pedhaa": "pedha"}
raw = "dhahi was sour, pedhaa packing ok"
text = raw.lower()
for bad, good in fixes.items():
text = text.replace(bad, good)
print(text)
Ravindra Bagale's Tip
India text messy — pehle small clean map + TF-IDF baseline. Perfect grammar nako wait karu.