Authors - Adarsh Singh Jadon, Rohit Agrawal, Aditya A. Shastri Abstract - 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 with 1D-CNN model. Combining the strengths of LSTM and GRU units along with 1D-CNN, demonstrated superior performance with an accuracy of 93.21%, precision of 93.57%, and recall of 93.02%, along with Sensitivity & Specificity of 93.62% and 93.24% respectively. It also achieved F1 Score of 93.44%. We also evaluated the model on some other parameters, such as PPV, PNV, RPV, and RNV. This model outperformed existing approaches, including the HF-CSA model from the SemEval-2020 dataset, which achieved an accuracy of 76.18%.
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