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

This paper presents a novel, dual-modal framework for sentiment and emotion analysis, capable of processing both textual data and static facial imagery.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, categorizing 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 performance 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.