With the widespread of online videos and digital transformation, video informatics and analytics have recently gained substantially increasing importance with an impressive success in a variety of tasks such as digital marketing, video surveillance and security systems, healthcare systems, talk show analysis, analysis of influencing groups in social media, and target tracking. This paper evaluates the potential contribution of various video modalities and how they are correlated to video analytics for sentiment analysis in the morphologically-rich Arabic language. Moreover, an enhanced approach is presented for video analytics to predict the speaker’s sentiment of multi-dialect Arabic through the integration of textual, auditory and visual modalities. Different features are extracted to represent each modality including prosodic and spectral acoustic features to represent audio, neural word embedding to represent audio text transcript, and dense optical-flow descriptors to represent visual modality. The extracted features are used individually to train two machine learning classifiers to provide a baseline. Then, the effectiveness of various combinations of modalities is verified using multi-level fusion (feature, score and decision). The experimental results demonstrate that the proposed approach of combining different modalities can lead to more accurate prediction of speaker’s sentiment with above 94% accuracy.

Opinion mining has gained increasing importance to draw insights from social media content to support decision making. Despite the explosive growth of efforts on linguistic analysis to detect and track people's opinions, more specifically when dealing with aspect-level opinion mining, the results are still away from generalization to real-world applications. Nowadays, emojis are getting excessively popular in social media communication as a complementary way to quickly express opinions and ideas in a visual manner. Two emoji-related issues are highlighted: ambiguity and misinterpretation of emojis' sentiment and tendency of persons to adopt emojis more in positive cases. This paper aims at investigating to what extent the usage of emojis can contribute to the automated detection of sentiment polarity of text messages with focus on Twitter posts in the Arabic language, a widely spoken language but has complex morphology and limited reliable resources for sentiment analysis. For this purpose, after an extensive review of the state-of-the-art of emojis-related work, a dataset is composed and several feature extraction methods are applied for both text and emojis modalities. Moreover, various early and late fusion techniques are proposed to combine both modalities at different levels including feature, score, decision and hybrid. The experimental results revealed that emojis features can significantly improve the classification results, especially when integrated with text at the score level.

Worker safety in the industrial, construction, and energy sectors is the primary concern of governments, organizations, and project managers. Traditional monitoring systems are tedious, laborious, subjective, expensive, and inaccurate. Therefore, the development of automated and accurate systems for real-time monitoring and detecting personal protective equipment (PPE) compliance is urgently needed. This study provides valuable insights and contributions to the field of workers’ safety in the industrial, construction, and energy sectors. It aims to evaluate the potential contribution of providing a robust and accurate real-time PPE compliance monitoring system using state-of-the-art deep learning techniques with real-world videos collected from surveillance cameras. Furthermore, it presents a systematic framework for developing, evaluating, and selecting the most suitable model for real-world applications, starting from data collection and ending with system deployment and maintenance. To the best of our knowledge, the collected dataset in this work is the largest PPE’s dataset, with approximately 7.7 million labels representing different PPEs in 386,011 video frames collected from several surveillance cameras. The techniques adopted to generate different models and evaluated for PPE are CenterNet, Vision Transformer, and YOLOv7. Moreover, an optimized real-time inference engine is developed leveraging the high-computing power of GPUs in the system. The experimental results demonstrate that the models developed using YOLO are the most accurate and can be better generalized to detect PPE compliance in new working environments, achieving mean average precisions of 92.36% and 66.70% on Test Set I and Test Set II , respectively. In addition, they achieve real-time detection of PPE compliance.