To enhance accuracy and contextual understanding

The gap

Evidence profile

Sourced from the limitations and future work of the source papers, classified as methodology gap, spanning 3 journals.

Supporting evidence — 3 representative gaps

• Multi-Modal Sentiment Analysis Using Text, Audio, And Facial Expressions for Human Emotion Detection - A Survey (2026) · International Journal for Research in Applied Science and Engineering Technology · doi

  The multimodal emotion recognition framework was trained and validated only on binary emotion classification, which oversimplifies emotional representation. Future research must extend the LSTM-based architecture to multi-class emotion recognition (e.g., joy, sadness, anger, fear, surprise, neutral) or dimensional emotion models (valence-arousal) to capture finer-grained emotional distinctions that better reflect real-world emotional complexity.

  methodology gaplimitationsevidence 5/5
  Keywords: multimodal emotion recognition LSTM binary classification multi-class dimensional emotion
• Employee Performance Classification and Monitoring using Machine zearning Models (2026) · International Journal of Science, Strategic Management and Technology · doi

  The paper mentions emotion recognition and fatigue monitoring as future enhancements but does not specify which machine learning models (e.g., CNN architectures, facial action units) or datasets will be integrated into the current keyboard/mouse activity-based classifier to enable multimodal emotion-fatigue detection.

  methodology gapfuture workevidence 5/5
  Keywords: emotion recognition fatigue monitoring machine learning classifier multimodal facial landmarks
• Real-Time Facial Emotion Detection Using Deep Learning and AI (2026) · International Journal of Mathematics And Computer Research · doi

  To enhance accuracy and contextual understanding, multimodal emotion recognition—which integrates facial, speech, and physiological cue—is emerging as a promising approach.

  methodology gapfuture workevidence 4/5
  Keywords: enhance accuracy contextual understanding multimodal emotion recognition integrates facial speech physiological emerging promising approach

Questions about this gap