Hinglish Sentiment Analysis Using Machine Learning: A FastText-BiLSTM Approach for Code-Mixed Social Media Text

Registration ID: IJNRD_324139

Published ID: IJNRD2604723

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ANKIT KAUSHIK, Rishabh Tyagi, Rishabh Nehra

Hinglish, Code-Mixed NLP, Sentiment Analysis, FastText, BiLSTM, Social Media Mining, Deep Learning, Indian Languages, Phonetic Normalization.

Bhai, tere research paper ka abstract ye raha: **Abstract —** Social media communication in India has evolved into a unique linguistic phenomenon known as Hinglish—a code-mixed blend of Hindi and English written primarily in Roman script. While Hinglish offers high emotional expressiveness, it creates a low-resource environment for traditional NLP tools due to non-standardized grammar, diverse phonetic spelling variations (e.g., 'karo' vs 'kro' vs 'karro'), and the pervasive use of regional slang and abbreviations. This paper proposes a robust, end-to-end deep learning architecture that combines a domain-specific Hinglish Normalization Lexicon, FastText sub-word embeddings (300-dimensional), and a stacked Bidirectional Long Short-Term Memory (BiLSTM) network for three-class sentiment classification. Our model is trained and evaluated on a meticulously curated, class-balanced dataset of 3,500 annotated samples sourced from Twitter and YouTube. The proposed system achieves a state-of-the-art accuracy of 86.4%—an 18.1% improvement over the Naive Bayes baseline—while maintaining a real-time end-to-end inference latency of 76.4 ms per sentence, confirming its viability for live social media stream analysis.

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ANKIT KAUSHIK, Rishabh Tyagi, & Rishabh Nehra (April-2026). Hinglish Sentiment Analysis Using Machine Learning: A FastText-BiLSTM Approach for Code-Mixed Social Media Text. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), h177-h182. https://ijnrd.org/papers/IJNRD2604723.pdf

Volume 11 Issue 4, April-2026

Pages : h177-h182

Paper Reg. ID: IJNRD_324139

Published Paper Id: IJNRD2604723

Research Area: Other area not in list

Author Type: Indian Author

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2604723.pdf

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