Sentimental Analysis using Text, Audio, Video Data

Sentimental analysis using audio and video has become an essential technique for understanding public opinion on online platforms. Unlike text-based methods, audio and video provide natural expressions such as tone, pitch, facial reactions, and behavioral cues, which help in identifying the true emotional state of a person. This work focuses on a dual-modality sentiment analysis system where audio is processed using speech-to-text conversion and linguistic feature extraction, while video frames are analyzed to detect facial expressions and behavioral cues. The results from both modalities are combined to produce a more accurate sentiment classification. This approach improves reliability, reduces noise-based errors, and provides a more realistic sentiment outcome for social media reviews and real-time interactions.