Title:Synergistic Sentiment Analysis: Integrating Textual Cues and Facial Expressions for Robust Emotion Classification


Authors:Rehan Ansari, Vandana Kate, Manoj Kumar Gupta, Chanchal Bansal


Published in: Volume 3 Issue 1 Jan June 2026, Page No.336-344


Keywords:Block Chain, Mobile Application, Data Privacy, Transparency


Abstract:This paper presents a novel, dual-modal framework for sentiment and emotion analysis, capa ble of processing both textual data and static facial im agery. The system integrates two specialized modules: a text analysis engine leveraging Natural Language Processing (NLP) via the TextBlob library to classify sentiment as Positive, Negative, or Neutral, and a computer vision module utilizing OpenCV and Deep Face for real-time facial emotion detection, categoriz ing expressions into Happy, Sad, Angry, Surprised, or Neutral. A key innovation is the implementation of an intuitive, emoji-based visualization layer that provides immediate, cross-modal interpretability of results. Developed in Python with a Streamlit web interface, the framework demonstrates robust perfor mance in bridging the gap between linguistic and visual affective computing. This work underscores the potential of integrated multi-modal systems to enhance applications in market analytics, customer service platforms, and human-computer interaction by providing a more holistic understanding of user sentiment.


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