Analysis of Emotional Cognitive Capacity of Artificial Intelligence

Emotional cognitive capacity is crucial for AI applications. Existing methods generally rely on indirect evaluation approaches to measure AI’s emotional cognitive capacity, lacking effective means for comprehensive evaluation. Therefore, in this work, we introduce Emo-Lens, a more intuitive, accurate, and interpretable method for assessing AI’s emotional cognitive capacity. Emo-Lens calculates the relevant influence weights of entities for emotional judgment by using an XAI (Explainable Artificial Intelligence) algorithm, which are then used to calculate what we term ’significance redundancy.’ By calculating significance redundancy, Emo-Lens assesses the AI model’s ability to perform human-like emotional cognition, achieving a more intuitive, accurate, and interpretable assessment of AI’s emotional cognitive capacity.We also validate the accuracy and sensitivity of our Emo-Lens approach through several knowledge-enhancement experiments on the sentiment analysis task. We explore whether AI models with knowledge enhancement can identify correct emotional cues as humans do when making emotional judgments. Our results demonstrate that Emo-Lens is an accurate method for evaluating AI’s emotional cognitive capacity, as it reveals that AI’s emotional cognition can be improved through knowledge-enhancement. This enhancement process mirrors human behavior in terms of focusing attention and integrating background knowledge during emotional understanding. Emo-Lens has deepened our understanding of AI’s emotional cognitive capacity to some extent.