A Psychological Rationale for our Approach
Empathy has two main components: (1.) Cognitive Empathy , the intellectual ability to understand another person’s emotions, thoughts, and motives. It involves the ability to comprehend someone else’s mental state and why they might be feeling a certain way, which is essential for effective communication and social interaction. (2.) Affective Empathy , the ability to physically feel another person’s emotions, often leads to emotional responses such as compassion or concern. For a more detailed discussion, see (Decety, 2005 ) .

This research focuses on Cognitive Empathy, aiming to classify the emotional state of a patient and enable the system to respond appropriately. Teaching machines to physically feel the emotions of others raises deep ethical and philosophical questions about the nature of consciousness and emotion in artificial systems, a topic that will likely remain at the forefront of futuristic research.

The approach aims to leverage and enhance the capabilities of empathetic LLMs by integrating emotion embeddings into their framework, guided by the intuition that the emotional spectrum is complex, and many emotions may coexist in a single sentence or piece of text.

We define an emotion embedding as a high-dimensional vector representing an emotional state. Unlike word embeddings Allen and Hospedales ( 2019 ) , which capture semantic meaning, emotion embeddings synthesize an individual’s emotional state within a conversation by encoding affective information. A distribution is generated by sampling and normalizing multiple predictions from a probabilistic classifier. These distributions can also be interpreted as embeddings, enabling meaningful algebraic operations. Complex emotions are encoded and represented by sequentially accumulating through the summation of many emotion embeddings, e.g., by accumulating over the many interactions that occur over an entire conversation (see Figure 2 for a visual explanation).

An emotion embedding can be defined as:

Emotion Embedding=\sum_{j}^{K}\alpha_{j}e_{j}

where \sum_{j}^{K}\alpha_{j}=1 , K is the total number of all the different type of emotions considered and e_{j} is a specific emotion.

The use of emotional profiles to assess whether patients suffer from mental disorders is not entirely new in psychometrics. This approach aligns with established psychological assessment methods, such as the Beck Depression Inventory-II (BDI-II) Beck et al. ( 1996 ) , a widely used tool for measuring depression severity. The BDI-II includes items evaluating various emotional states and symptoms, like sadness, pessimism, guilt, agitation, irritability, and indecisiveness. Each item contributes to an overall score, aiding in the formulation of a final diagnosis.

For example, in the BDI-II, a patient might score high on sadness and pessimism, while moderate on guilty feelings and irritability.
Similarly, our approach creates an emotional profile capturing the interplay of various emotions, offering a comprehensive view of an individual’s mental state.

Thus, our work reveals a key insight: emotions act as indicators of deeper, complex mental states.

This study aims to demonstrate that mental states can be represented as collections of different emotions. Therefore, explainable emotion embeddings can be useful not only in identifying individuals in need of assistance but also as a potentially effective tool for automated diagnosis.

This multidimensional approach to emotional assessment acknowledges the complexity of human psychology, where a single emotional label cannot fully capture an individual’s experience. By analyzing the emotional distribution, our system provides insights that align with the subtle understanding of mental states in clinical psychology, potentially enabling more accurate and personalized mental health support.