Emotion Management Module

• Emotion Management Module is a computational component that models, tracks, and controls user emotions using dialogue history and nuanced intensity levels.
• It captures real-time emotional states, quantifies shifts using ΔE, and performs causal analysis to inform adaptive response strategies.
• EMMs integrate with empathetic dialogue systems, psychological counseling agents, and multimodal speech synthesis to enhance AI empathy, safety, and performance.

An Emotion Management Module (EMM) is a computational component designed to model, track, and, when applicable, control the emotional state dynamics of users or dialogue participants within conversational, empathetic, or affective artificial intelligence systems. EMMs have become central to LLM-based psychological counselors, task-oriented dialogue agents, empathetic response generators, and multi-modal reasoning systems, where the real-time recognition, interpretation, and coherent handling of nuanced interpersonal affect is critical for downstream task efficacy and safety.

1. Core Functions and Scope

An EMM is responsible for capturing the current emotion state (including primary and secondary emotions with intensity or valence levels), tracking emotion shifts longitudinally throughout a session, modeling or inferring the underlying causes or triggers, and storing this trajectory as a persistent context or “Emotion Memory.” The output of the EMM directly informs response selection, risk analysis, and workflow adaptation (Xia et al., 18 Jan 2026, Peng et al., 2022, Hu et al., 2022, Liu et al., 2022).

Primary EMM objectives:

• State capture: Identify the user’s or agent’s present dominant and secondary emotions, often mapped to a theoretically robust lexicon (e.g., Plutchik’s eight-emotion wheel).
• Shift and trend tracking: Quantify and record moment-to-moment changes (ΔE) and summarize overall emotional trajectories and reversals.
• Causal analysis: Attribute short, contextually relevant rationales to detected emotional changes, often referencing triggers or unmet needs.
• Persistent memory: Maintain an emotion-history storage structure to ensure response consistency, longitudinal trend analysis, and context coherence.

2. Architectural Design Patterns

2.1 Modular Subcomponents

A reference architecture, as in the PsychēChat system (Xia et al., 18 Jan 2026), decomposes the EMM into two tightly coupled modules:

• Emotion Tracking Agent (ETA): Consumes the latest dialogue turn, full history, and current Emotion Memory, outputting structured records comprising current state, recent shifts, and causal notes.
• Emotion Memory: Implements a persistent key-value store indexed by dialogue turn, holding emotion labels (primary/secondary), intensities, computed shifts, and causal rationales.

Other paradigms, such as MM-DFN (Hu et al., 2022), adopt graph-based dynamic fusion modules that manage intra- and inter-modal emotion interactions for multimodal conversational input, while state management modules in empathetic dialogue systems leverage transformer-based trackers to model user emotion and intent shifts (Liu et al., 2022).

2.2 Data Flow and Interface

Typical sequential EMM operation (PsychēChat, Agent Mode):

1. Receive the latest input message and dialogue history.
2. Invoke the ETA module via tool-call or prompt, extracting emotion(s), calculating ΔE, summarizing trends, and justifying causes.
3. Store results in Emotion Memory; pass the updated trajectory to the counselor or policy module.
4. In LLM chain-of-thought mode, prepend the output with an “Emotion Shift Tracking” section whose intermediate results drive planning and safety assessments.

2.3 Model Choices

Most recent implementations employ large-scale pretrained LLMs (e.g., Qwen3-8B (Xia et al., 18 Jan 2026), RoBERTa (Peng et al., 2022)) fine-tuned with task-specific emotion annotation corpora. In multimodal or speech synthesis contexts, the EMM is distributed across distinct feature pipelines for text, audio, and visual signals, feeding a sequence-to-sequence (seq2seq) backbone with multi-level emotion embeddings (Hu et al., 2022, Lei et al., 2022).

3. Mathematical Formalism

3.1 Discrete Emotion Classification

Given turn embedding $x_t$, the module outputs categorical and intensity emotion distributions:

$p_t = \mathrm{softmax}(W x_t + b)$

Emotion label prediction applies top-$k$ selection from $p_t$. Fine-tuning is supervised using cross-entropy loss:

$L_{\text{class}} = -\sum_i y_i \log p_i$

where $y_i$ is the one-hot ground truth label (Xia et al., 18 Jan 2026).

3.2 Emotion Shift and Trend Quantification

Emotion shifts are encoded numerically:

$\Delta E_t = E_t - E_{t-1}$

$E_t$ is a mapping from categorical-intensity emotion to a scalar (e.g., “Sadness-Moderate” $\rightarrow$ $-2$). Trend summaries are extracted using a sliding window:

$$\text{trend} = \mathrm{sign} \left(\sum_{i=t-K+1}^t \Delta E_i\right)$$

3.3 Fusion and Memory

Emotion Memory update:

$$\text{EmotionMemory}[t] \leftarrow (E_t, \Delta E_t, \text{rationale}_t)$$

In joint intention-emotion models, emotion features are fused with inferred intentions using concatenation, element-wise operations, and MLPs:

$$f_i = \mathrm{tanh}(W_f^T [s_i; h_i; s_i \circ h_i; s_i - h_i] + b_f)$$

where $s_i$ and $h_i$ represent current and historical intention vectors, respectively (Peng et al., 2022).

3.4 Multimodal and Hierarchical Encoding

Multimodal EMMs use graph structures. Let $x_i^m$ be the node encoding for modality $m$ at utterance $i$:

$x_i^m = c_i^m + \gamma^m s_i^m$

where $c_i^m$ is a context vector and $s_i^m$ is a speaker state. Dynamic graph fusion layers employ gating and graph convolutions to iteratively update representations (Hu et al., 2022).

Speech synthesis EMMs decompose emotion into global ($h_{\text{global}}$), utterance ($h_{\text{utt}}$), and local ($h_{\text{local}}$) embeddings fused at each decoder step, allowing both prediction and manual control of expressivity (Lei et al., 2022).

4. Training Regimes and Data Annotation

4.1 Dataset Construction

• PsychēDialog (Xia et al., 18 Jan 2026): 1,003 counseling dialogues annotated per turn for primary and secondary Plutchik-based emotions (with intensity), deltas, and rationales.
• Empathetic response datasets with explicit emotion and intent shift matrices, multi-label schema for emotion-to-intent transitions (Liu et al., 2022).
• Speech corpora with syllable-level emotion intensity annotations, used for multi-scale supervision in text-to-speech (Lei et al., 2022).

4.2 Supervision and Optimization

Supervised learning is used for both the emotion classification and shift prediction paths. Joint multi-task loss functions are applied in models handling intention and emotion together:

$L(\theta) = \lambda_1 L_{m} + \lambda_2 L_{e}$

where $L_m$ and $L_e$ are cross-entropy losses for intention and emotion predictions, respectively (Peng et al., 2022). Speech-oriented EMMs also use L2 regression for scalar intensity (Lei et al., 2022). Standard optimizers (AdamW) and learning rate schedules are used, with curriculum or focal-loss to handle class imbalance as needed (Xia et al., 18 Jan 2026, Hu et al., 2022).

5. Integration with Downstream Agents and Applications

5.1 Conversational Counseling

In psychological counseling LLMs, the EMM operates as the foundational analytic stage. Agent Mode infrastructures receive ETA outputs and update the Emotion Memory, directly guiding the downstream EFT Counselor Agent for strategy selection. Risk Control Modules read the same memory for safety assessment, triggering revision steps if risk markers are present (Xia et al., 18 Jan 2026).

In end-to-end LLM Mode, emotion shift analysis is prepended to each chain-of-thought, ensuring emotion-aware reasoning per generation cycle.

5.2 Empathetic Dialogue and Policy Management

Emotion state trackers and policy predictors are integrated so that predicted source emotion, target listener emotion, and anticipated intent modulate the knowledge and strategy layers of response decoders in dialog systems. Feature fusion layers and gating mechanisms dynamically combine these signals for token-level generation (Liu et al., 2022).

5.3 Multimodal and Prosody-Conditioned Speech Generation

Multi-scale EMMs in text-to-speech condition the decoder on global, utterance, and local emotion vectors. These modules enable not only accurate transfer and prediction of prosodic emotion but also user-driven manual control of expressivity in the synthesized speech output (Lei et al., 2022).

6. Empirical Assessment and Ablation Analyses

The inclusion and design of EMMs have been empirically shown to yield significant gains:

• PsychēChat (Ablation study): Removing EMM leads to Sentient score drop (78.56 → 72.93), increased dialogue failures (9 → 14), and lower empathy and skill subscores in ESC-Eval (Xia et al., 18 Jan 2026).
• RAIN (Joint Intention-Emotion Modeling): Emotion F1 improves from 59.13% (RoBERTa baseline) to 64.07% (+4.94 pts) when inter-intention relations are jointly modeled (Peng et al., 2022).
• MM-DFN: Dynamic graph-based emotion fusion elevates weighted F1 on IEMOCAP from 62.89 (DialogueGCN) to 68.18; ablation removing the core EMM reduces F1 by 4.38 (Hu et al., 2022).
• Empathetic Response Generation: Exclusion of intent or emotion-fusion paths reduces BLEU, BERTScore, and response diversity, demonstrating their architectural necessity (Liu et al., 2022).
• Multi-scale Speech Synthesis: Text-predicted emotion, local expressivity control, and flexible prediction/training strategies outperform fixed-embedding baselines (Lei et al., 2022).

7. Case Studies and Representative Examples

A condensed counseling example demonstrates key EMM operations (Xia et al., 18 Jan 2026):

• Turn 1 (“I keep feeling anxious and my chest feels tight…”): ETA yields Fear (moderate) + Anticipation (mild), with cause “fear of repeating past rejection.”
• Turn 6 (“Actually, talking this out makes me feel a little more hopeful.”): Joy (mild) + Trust (mild); positive ΔE, trend reversal.
• Updates to Emotion Memory signal strategic shift to the counselor agent, enabling tailored empathic intervention at the moment of emotional improvement.

Multimodal EMMs show improved identification of emotion shifts across speech, text, and video, while maintaining consistency across diverse user populations (Hu et al., 2022, Lei et al., 2022).

---

The Emotion Management Module, in its contemporary LLM- and graph-based forms, is a key enabler for affective computing systems requiring sustained, coherent, and controllable emotional reasoning. Its technical evolution has contributed to measurable advancements in empathy, response safety, and real-time affect tracking in both conversational and multimodal generation tasks (Xia et al., 18 Jan 2026, Peng et al., 2022, Hu et al., 2022, Liu et al., 2022, Lei et al., 2022).