Multimodal sentiment and gender classification for video logs

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.