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Sentimental Analysis using Text, Audio, Video Data
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1712512
Abstract
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.
How to cite this paper
@article{1712512,
author = {Pooja B R, Prajwal Gowda N, R M Guruprasad, Tabreek Malk, Abdul Rehaman},
title = {Sentimental Analysis using Text, Audio, Video Data},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {55-57},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712512.pdf},
abstract = {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.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712512}
}