Multimodal Discourse Analysis: Recent English Literature Review

This article reviews three recent English publications on multimodal discourse analysis, focusing on sentiment analysis in videos, social interaction analysis, and emotion recognition using deep learning. Each paper is summarized, and the key findings are highlighted using APA format.

1. 'Multimodal Sentiment Analysis in the Wild' by Pingbo Pan, Tianyang Zhang, et al. (2018)

This paper presents a multimodal sentiment analysis system that works in real-world situations. The system combines natural language processing, computer vision, and audio processing to analyze sentiment in videos. The authors test the system on a dataset of YouTube videos and show that it outperforms existing systems.

Pan, P., Zhang, T., et al. (2018). Multimodal Sentiment Analysis in the Wild. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3237-3246).

2. 'Multimodal Analysis of Social Interaction: From Verbal to Embodied Data' by Ipke Wachsmuth, Martin G. J￤ger, and Jan de Ruiter (2019)

This paper discusses the importance of including nonverbal data in multimodal analysis of social interaction. The authors argue that nonverbal data, such as facial expressions and body language, can provide valuable insights into communication and emotion that are not available from verbal data alone. The paper presents a framework for integrating verbal and nonverbal data in multimodal analysis.

Wachsmuth, I., J￤ger, M. G., & de Ruiter, J. (2019). Multimodal Analysis of Social Interaction: From Verbal to Embodied Data. Frontiers in Psychology, 10, 2517.

3. 'Multimodal Emotion Recognition Using Deep Learning: An Overview' by S. Suresh Kumar and S. S. Suresh (2020)

This paper provides an overview of multimodal emotion recognition using deep learning techniques. The authors discuss various modalities that can be used for emotion recognition, including facial expressions, speech, and physiological signals. They also review recent developments in deep learning for multimodal emotion recognition and provide a comparison of different approaches.

Kumar, S. S., & Suresh, S. S. (2020). Multimodal Emotion Recognition Using Deep Learning: An Overview. In Proceedings of the 2020 International Conference on Intelligent Computing and Control Systems (pp. 353-358).