hinglish-nlp 0.2.0

Hinglish (Roman Hindi + English) NLP toolkit - Sentiment, Emotion, Sarcasm & more

hinglish-nlp 🇮🇳

A powerful NLP toolkit for Hinglish (Roman Hindi + English) text analysis.

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Installation

pip install hinglish-nlp

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Features

Feature	Description
✅ Sentiment Analysis	Positive / Negative / Neutral / Mixed
✅ Emotion Detection	Joy, Anger, Sadness, Fear, Surprise, Disgust
✅ Sarcasm Detection	Pattern + contradiction based
✅ Language Mix Detection	Hinglish vs English ratio
✅ Transliteration	Roman Hindi → Devanagari
✅ Batch Processing	Multiple texts at once
✅ Key Phrase Extraction	Important phrases from text
✅ Intensity Score	0.0 to 1.0 scale
✅ Confidence Score	How sure the model is

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Usage

Basic Sentiment Analysis

from hinglish import analyze

result = analyze("yaar bahut mast movie thi!")
print(result["mood"])        # positive
print(result["emoji"])       # 😊
print(result["intensity"])   # 0.45
print(result["confidence"])  # 0.75

Emotion Detection

from hinglish import detect_emotion

emotions = detect_emotion("mujhe bahut gussa aa raha hai!")
print(emotions)  # {'anger': 0.35}

emotions = detect_emotion("aaj bahut khushi hui yaar!")
print(emotions)  # {'joy': 0.7}

Sarcasm Detection

from hinglish import is_sarcastic

result = is_sarcastic("haan bilkul, bahut accha hai na!!")
print(result)
# {'is_sarcastic': True, 'confidence': 0.6}

Language Mix Detection

from hinglish import detect_language

mix = detect_language("yaar ye movie bahut boring thi")
print(mix)
# {'hinglish': 0.5, 'english': 0.33, 'unknown': 0.17}

Transliteration (Roman → Devanagari)

from hinglish import transliterate

text = transliterate("mera naam lalit hai")
print(text)  # मेरा नाम ललित है

Batch Processing

from hinglish import analyze_batch

texts = [
    "yaar mast movie thi!",
    "bilkul bakwaas tha yaar",
    "theek thak tha, kuch khaas nahi"
]

results = analyze_batch(texts)
for r in results:
    print(r["mood"], r["emoji"])
# positive 😊
# negative 😠
# neutral  😐

Full Analysis

from hinglish import analyze

result = analyze("Phone ki battery toh bekar hai but camera mast hai")
print(result)
# {
#   'mood': 'mixed',
#   'intensity': 0.3,
#   'confidence': 0.75,
#   'emoji': '🤨',
#   'sentiment': 'mixed',
#   'key_phrases': ['Phone ki battery toh bekar hai but camera mast hai'],
#   'sarcasm': False,
#   'sarcasm_confidence': 0.0,
#   'language_mix': {'hinglish': 0.36, 'english': 0.55, 'unknown': 0.09},
#   'category': 'mixed',
#   'summary': 'A detailed Hinglish message expressing mixed sentiment...',
#   'emotions': {'disgust': 0.35},
#   'word_count': 11,
#   'positive_words_found': ['mast'],
#   'negative_words_found': ['bekar'],
#   'transliteration': 'Phone की battery तो bekar है but camera मस्त है'
# }

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Output Fields

Field	Type	Description
mood	str	positive / negative / neutral / mixed
intensity	float	0.0 – 1.0
confidence	float	0.0 – 1.0
emoji	str	Visual mood indicator
sentiment	str	Same as mood
key_phrases	list	Important phrases
sarcasm	bool	Is text sarcastic?
sarcasm_confidence	float	Sarcasm confidence score
language_mix	dict	hinglish / english / unknown ratio
category	str	praise / complaint / casual / mixed
summary	str	Short summary of the text
emotions	dict	Detected emotions with scores
word_count	int	Total word count
positive_words_found	list	Positive words detected
negative_words_found	list	Negative words detected
transliteration	str	Roman → Devanagari

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Author

Lalit — lalitpal2206

License

MIT License