ER-SMTIV
Develop and implement an intelligent emotion recognition system utilizing natural language processing, deep learning-based image analysis, and behavioural pattern mining to accurately identify human emotions expressed through social media posts, comments, emojis, and shared images (e.g., Twitter sentiment analysis systems and Instagram emotion analytics platforms). Integrate multimodal learning techniques that combine textual and visual features to enhance emotion classification accuracy and reliability. Establish a real-time monitoring and visualization framework for tracking emotional trends and detecting critical psychological states such as stress, anxiety, and depression across online communities (e.g., mental health monitoring systems using social media analytics). Incorporate cultural-aware learning and adaptive models to handle regional language variations, sarcasm, and diverse emotional expressions using advanced machine learning, computer vision, and transformer-based deep learning architectures.
Privacy and Ethical Concerns: Emotional data extracted from social media content is highly sensitive, and ensuring user consent, secure data storage, anonymization, and compliance with data protection regulations is essential.
Cultural and Linguistic Variations: Differences in language usage, slang, code-mixing, emojis, and culturally specific expressions of emotions can lead to misclassification and reduced system accuracy.
Data Noise and Ambiguity: social media content often contains informal language, sarcasm, irony, memes, and ambiguous images, which makes reliable emotion interpretation challenging.
Multimodal Data Fusion Complexity: Combining text, image, and behavioural features introduces challenges in feature alignment, synchronization, and decision-level integration.
Bias and Dataset Imbalance: Emotion datasets may be biased toward specific regions, age groups, or languages, leading to unfair or inaccurate predictions.
Scalability and Real-Time Processing: Handling large volumes of streaming social media data in real time requires high computational resources and efficient model optimization.
Team Lead
Dr. Dharmendra Sharma
Team Members
Email ID:rajgurav.mishra@nmims.edu
Email ID:mahipal.gadhavi@nmims.edu