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Authors: Sadam Al-Azani and El-Sayed El-Alfy

Affiliation: College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261 and Saudi Arabia

ISBN: 978-989-758-350-6

Keyword(s): Multimodal Recognition, Sentiment Analysis, Opinion Mining, Gender Recognition, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Vision and Perception

Abstract: Sentiment analysis has recently attracted an immense attention from the social media research community. Until recently, the focus has been mainly on textual features before new directions are proposed for integration of other modalities. Moreover, combining gender classification with sentiment recognition is a more challenging problem and forms new business models for directed-decision making. This paper explores a sentiment and gender classification system for Arabic speakers using audio, textual and visual modalities. A video corpus is constructed and processed. Different features are extracted for each modality and then evaluated individually and in different combinations using two machine learning classifiers: support vector machines and logistic regression. Promising results are obtained with more than 90% accuracy achieved when using support vector machines with audio-visual or audio-text-visual features.

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Paper citation in several formats:
Al-Azani, S. and El-Alfy, E. (2019). Multimodal Sentiment and Gender Classification for Video Logs.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 907-914. DOI: 10.5220/0007711409070914

@conference{icaart19,
author={Sadam Al{-}Azani. and El{-}Sayed M. El{-}Alfy.},
title={Multimodal Sentiment and Gender Classification for Video Logs},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={907-914},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007711409070914},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multimodal Sentiment and Gender Classification for Video Logs
SN - 978-989-758-350-6
AU - Al-Azani, S.
AU - El-Alfy, E.
PY - 2019
SP - 907
EP - 914
DO - 10.5220/0007711409070914

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