Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification

Automatic detection of consumers’ complaints about items or services they buy can be critical for organizations and online merchants. Previous studies on complaint identification are limited to text. Images along with the reviews can provide cues to identify complaints better, thus emphasizing the importance of incorporating multi-modal inputs into the process. Generally, the customer’s emotional state significantly impacts the complaint expression; thus, the effect of emotion and sentiment on complaint identification must also be investigated. Furthermore, different organizations are usually not allowed to share their privacy-sensitive records due to data security and privacy concerns. Due to these issues, traditional models find it hard to understand and identify complaint patterns, particularly in the financial and healthcare sectors. In this work, we created a new dataset - Multi-modal Complaint Dataset (MCD), a collection of reviews and images of the products posted on the website of the retail giant Amazon. We propose a federated meta-learning-based multi-modal multi-task framework for identifying complaints considering emotion recognition and sentiment analysis as two auxiliary tasks. Experimental results indicate that the proposed approach outperforms the baselines and the state-of-the-art approaches in centralized and federated meta-learning settings.