A DEEP NEURAL FRAMEWORK FOR EMOTION DETECTION IN HINDI TEXTUAL DATA

Emotion identification from textual data is crucial for understanding human emotions in applications such as user behaviour analysis, targeted content delivery, and mental health monitoring. Even though English has advanced significantly, emotion detection in Hindi, a widely spoken language lacking enough resources, is still tricky because of data, grammatical complexity, and NLP tools. This study presents a deep neural network (DNN)-based framework for emotion classification in Hindi sentences. Our approach includes Hindi-specific preprocessing using the iNLTK library, comparative evaluation of multiple encoding techniques (Bag of Words, TF-I D F, Word2Vec), and training a robust DNN model to classify text into five emotion classes: joy, sadness, anger, suspense, and neutral. Experimental results on the BHAV dataset demonstrate that our DNN model achieves a balanced accuracy of 94.91%, outperforming traditional classifiers such as Naive Bayes, SVC, Logistic Regression, Decision Trees, and Boosted Trees. The confusion matrix and training-validation curves confirm the model's generalization capabilities and minimal overfitting. Our outcomes underscore the significance of deep learning in low-resource language settings and set the groundwork for future improvements in multimodal emotion detection, code-mixed data handling, and deployment in real-time Hindi NLP applications.