• Adarsh Singh Jadon
• Prof. Mahesh Parmar
• Dr. Rohit Agrawal

This study investigated the efficacy of various deep learning models in performing sentiment analysis on code-mixed Hinglish text, a hybrid language widely used in digital communication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the SemEval-2020 Task 9 competition and employs models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed Hybrid LSTM-GRU model. The study's primary objective is to developed a robust sentiment analysis framework that accurately classifies sentiment in Hinglish text. The Hybrid LSTM-GRU model, combining the strengths of LSTM and GRU units, demonstrated superior performance with an accuracy of 92%, precision of 92.29%, and recall of 92.31%. This model outperformed existing approaches, including the HF-CSA model from the SemEval-2020 dataset, which achieved an accuracy of 76.18%. The results highlight the proposed model's capability to handle the nuances of Hinglish, including its informal and code-mixed nature, more effectively than traditional models. This study sets a new benchmark for sentiment analysis in multilingual contexts and underscores the importance of advanced deep learning techniques in tackling the challenges of code-mixed language processing.

Hinglish, Sentiment Analysis, Deep Learning, Code-Mixed Language, RNN, LSTM, GRU, BERT, Neural Networks, Multilingual NLP, Informal Language Processing.

"Advanced Deep Learning Approaches for Sentiment Analysis in Code-Mixed Hinglish Text: A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.h127-h132, July-2024, Available :http://www.jetir.org/papers/JETIR2407717.pdf

"Advanced Deep Learning Approaches for Sentiment Analysis in Code-Mixed Hinglish Text: A Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. pph127-h132, July-2024, Available at : http://www.jetir.org/papers/JETIR2407717.pdf