  Multimodal sentiment analysis aims to recognize people's attitudes from
multiple communication channels such as verbal content (i.e., text), voice, and
facial expressions. It has become a vibrant and important research topic in
natural language processing. Much research focuses on modeling the complex
intra- and inter-modal interactions between different communication channels.
However, current multimodal models with strong performance are often
deep-learning-based techniques and work like black boxes. It is not clear how
models utilize multimodal information for sentiment predictions. Despite recent
advances in techniques for enhancing the explainability of machine learning
models, they often target unimodal scenarios (e.g., images, sentences), and
little research has been done on explaining multimodal models. In this paper,
we present an interactive visual analytics system, M2Lens, to visualize and
explain multimodal models for sentiment analysis. M2Lens provides explanations
on intra- and inter-modal interactions at the global, subset, and local levels.
Specifically, it summarizes the influence of three typical interaction types
(i.e., dominance, complement, and conflict) on the model predictions. Moreover,
M2Lens identifies frequent and influential multimodal features and supports the
multi-faceted exploration of model behaviors from language, acoustic, and
visual modalities. Through two case studies and expert interviews, we
demonstrate our system can help users gain deep insights into the multimodal
models for sentiment analysis.
