Background: Polarity-based sentiment analysis (positive/neutral/negative)

Early sentiment analysis research primarily focused on polarity classification, categorizing text as positive, neutral, or negative. This framework proved effective for tasks such as opinion mining and product review analysis [13], but it fails to capture subtle emotional gradients, irony, or mixed expressions common in social media discourse. Moreover, polarity-based models overlook compositional linguistic cues (e.g., contrastive conjunctions or sarcasm) and cultural affective nuances, thereby motivating a transition toward richer emotion taxonomies and hierarchical sentiment representations.

Fine-grained sentiment analysis

Fine-grained sentiment analysis extends traditional polarity classification by identifying specific emotions such as joy, sadness, anger, fear, surprise, and disgust. This paradigm shift from coarse polarity to affective granularity has been driven by emerging applications in mental health monitoring, hate speech detection, and social media analytics, where systems must detect subtle affective signals rather than general sentiment polarity [13].

In response, diverse emotion taxonomies and frameworks have been developed. The Ekman model, Plutchik’s wheel, and the Geneva Emotion Wheel establish psychologically grounded emotion structures, while data-driven resources such as GoEmotions define 28 emotion categories derived from Reddit posts [14]. These taxonomies underpin supervised multi-label classification approaches. However, fine-grained emotion detection remains challenging due to label imbalance, emotional co-occurrence, and contextual ambiguity across cultures and social domains.

Early neural architectures based on RNNs and CNNs have been succeeded by transformer-based models such as BERT or RoBERTa fine-tuned on emotion corpora. Despite achieving higher macro-F1 scores [15], these models often overfit lexical features and struggle with contextual ambiguity, sarcasm, and multi-emotion overlap. Consequently, researchers are exploring modular and multi-step reasoning frameworks that improve interpretability and capture inter-emotion dependencies [16].

Recent research (2024–2025) has further advanced fine-grained emotion understanding through multimodal and cross-domain modeling. Liu et al. [17] proposed MDKAT, a multimodal decoupling framework with knowledge aggregation and transfer for video emotion recognition. Xu et al. [18] introduced diversified prompt composition for image emotion classification, while Guo et al. [19] presented a unified generative framework for multimodal emotion understanding. Beyond vision, Zhao et al. [20] applied EEG-based emotion recognition using autoencoder fusion and MSC-TimesNet modeling. In the realm of cross-modal sentiment analysis, Ma et al. [21] developed a contrastive fusion network with dynamic semantic diffusion. For education-specific emotion analysis, Liu et al. [22] employed graph neural networks with multi-head cross-attention. Addressing data quality challenges, Jiang and Yin [23] tackled occluded expression recognition using adversarial networks and dictionary learning in robot environments. Beyond vision, Zhao et al. [18] applied EEG-based emotion recognition using autoencoder fusion and MSC-TimesNet modeling, and Li et al. [24] employed reinforcement learning to intervene in negative emotion contagion on social networks. Together, these studies highlight a shift toward knowledge-enhanced, interpretable, and cross-modal emotion reasoning—directions that align closely with the multi-agent sentiment coordination framework proposed in this paper.

Large language models for sentiment tasks

Large-scale pre-trained language models (LLMs) such as GPT-3, ChatGPT, and LLaMA have revolutionized NLP and shown impressive few-shot and zero-shot capabilities in sentiment classification [25]. These models internalize affective patterns from massive corpora and produce contextually coherent sentiment judgments even for novel or low-resource settings. Their long-context reasoning and implicit world knowledge make them attractive candidates for fine-grained emotion recognition.

Nevertheless, LLMs exhibit several limitations when applied to sentiment tasks. They may produce inconsistent predictions across paraphrased inputs, struggle with compositional sentiment (e.g., “I love the food but hate the service.”), and display unstable emotional calibration. Moreover, their monolithic architectures prevent modular decomposition of sentiment reasoning and hinder interpretability. While prompt engineering and parameter-efficient fine-tuning techniques such as LoRA and AdapterFusion [26] partially alleviate these issues, the underlying reasoning remains opaque. These challenges have motivated the development of hybrid and multi-agent architectures that combine LLM expressiveness with explicit modular reasoning and coordination.

Multi-agent NLP systems

Multi-agent systems (MAS) have emerged as a promising paradigm for complex NLP tasks requiring distributed reasoning, contextual disambiguation, or collective decision-making. Instead of a single monolithic model, MAS architectures delegate subtasks to specialized agents and integrate their outputs through mechanisms such as debate, voting, or confidence-weighted fusion [27]. This approach reflects human cognitive processes, where perception, memory, and reasoning operate in parallel yet cooperatively.

In sentiment analysis, MAS has been applied to multi-perspective tasks such as dialogue understanding, misinformation detection, and hate speech classification, where independent agents can provide complementary or corrective interpretations. These systems improve calibration, enable modular debugging, and enhance robustness via agent diversity.

However, most existing MAS frameworks either employ static transformer heads without communication or rely on rule-based coordination. Few have integrated modern LLMs into dynamic agent collaborations capable of iterative reasoning and belief revision. Our proposed framework addresses this gap by adopting LLaMA-3.3-70B-Instruct as the backbone for three specialized agents—Perception, Reasoning, and Conflict Resolution—each fine-tuned for distinct roles. By combining LLM-level language understanding with explicit inter-agent coordination, this framework advances fine-grained, interpretable sentiment reasoning across heterogeneous social media environments.

Problem formulation

The core task addressed in this work is fine-grained sentiment classification over social media posts, which requires identifying one or more emotional categories conveyed within a given unit of user-generated content. Formally, let the input be a post , where denotes the space of textual (and optionally multimodal) utterances. Each post is associated with a latent set of emotions , where represents the predefined emotion taxonomy, and K is the number of emotion categories (e.g., 28 for GoEmotions). The model’s objective is to learn a mapping function that returns a subset of relevant emotions for any input x.

This problem is inherently multi-label, requiring the model to handle overlapping emotional constructs and recognize implicit sentiment signals. The complexity increases with inputs involving sarcasm, negation, indirect affect, or multi-aspect discourse. Let the model predict a probabilistic distribution , where θ represents trainable parameters and is the predicted probability of the k-th emotion. We employ a binary cross-entropy loss to optimize the model across multiple labels:

(1)

This formulation supports soft probability calibration and is compatible with threshold-based decision rules at inference time. To adapt the system to noisy labels and class imbalance, we further integrate a dynamic focal loss variant:

(2)

where is a class-specific weighting factor and γ controls the degree of penalization on well-classified examples. Unlike the static focal formulation, the variant introduces adaptive weighting based on both class frequency f_k and training progression t. Specifically, the coefficients evolve as and , allowing the model to gradually emphasize minority emotions as training advances. This instance-level adaptation ensures proportional gradient updates and stabilizes convergence across heterogeneous emotion distributions.

In cases where the input includes auxiliary information such as image embeddings or thread context , the input representation is extended via a concatenated multi-modal encoding: , where denotes a fusion encoder built on cross-attention layers. The mapping function is accordingly redefined as . The architecture must therefore operate under partial observability and noisy supervision, requiring robust latent reasoning components [28].

The task formulation also permits hierarchical reasoning by predicting both coarse (Ekman-style) and fine-grained (GoEmotions) categories. We define a dependency matrix , where C is the number of coarse categories. Let denote the aggregated score for coarse class c. This dual-level representation supports consistency regularization and is optimized jointly via:

(3)

where , , and are non-negative scalar weights controlling the relative importance of the fine-grained, coarse-level, and focal loss components, respectively. In our experiments, and are tuned such that , with the default setting unless otherwise specified.

Hierarchical emotion taxonomy and graph regularization

The selection of an emotion taxonomy is non-trivial in the context of fine-grained sentiment detection, particularly for social media. We adopt a hybrid taxonomy that combines the empirical breadth of GoEmotions with the psychological structure of Ekman’s basic emotions. Let be the fine-grained emotion set and the set of six supercategories: joy, sadness, anger, fear, disgust, and surprise. We define a bipartite graph , where adjacency matrix maps fine emotions to their coarse parent nodes.

This graph structure enables us to model label dependencies through structured inference. For example, if an emotion such as “gratitude” is predicted with high confidence, then the score for “joy” should also be elevated. During training, we enforce consistency by introducing a graph-based regularizer:

(4)

where is the aggregated score for supercategory c_j. This regularization ensures coherent predictions across hierarchical emotion paths and encourages low-entropy assignments.

To support inter-label reasoning, we construct a semantic similarity matrix where , with representing pretrained emotion embeddings (e.g., BERT or ConceptNet vectors). We define a smoothness constraint to promote semantically aware classification:

(5)

This term encourages similar emotion classes (e.g., “embarrassed” and “ashamed”) to receive proportionally close probabilities, improving generalization on sparse or ambiguous examples.

The inclusion of graph-based and smoothness regularization terms is theoretically grounded in manifold regularization [29], which enforces local smoothness among semantically or hierarchically related emotions (e.g., “joy” ↔ “excitement”). This mechanism mitigates over-segmentation of affective subclusters and promotes label consistency across the emotion hierarchy by minimizing prediction variance along the intrinsic geometry of the emotion manifold. Accordingly, the smoothness constraint complements the Laplacian term by stabilizing hierarchical dependencies between coarse and fine-grained emotion representations, leading to more consistent and interpretable classification outcomes.

The taxonomy also supports multilingual adaptation. Let be a language-specific emotion mapping, where . This allows us to train multilingual agents on aligned taxonomies while retaining universal semantic grounding. We evaluate taxonomy transferability using cross-lingual divergence:

(6)

This facilitates both zero-shot generalization and fine-tuned specialization, allowing the proposed system to adapt to the linguistic and cultural nuances of emotion expression.

Multi-agent sentiment reasoning setup

The proposed method adopts a three-stage multi-agent reasoning protocol: perception, analysis, and coordination. Each agent is instantiated as a lightweight LoRA-tuned version of LLaMA-3.3-70B-Instruct, specialized via modular objectives. Let denote the set of agents, where each A_i computes partial predictions . The global output is aggregated via a coordination function , learned through confidence calibration and meta-inference.

The perception agent A₁ performs span-level extraction. Given an input x, it outputs a matrix , where P_jk represents the attention score of token j toward emotion k. These scores are normalized via a token-level softmax:

(7)

The reasoning agent A₂ processes contextual cues and outputs intermediate logits . It employs masked contrastive objectives to disambiguate conflicting emotions:

(8)

where and are positive and negative prototypes derived from emotion centroids, and is cosine similarity.

The resolver agent A₃ takes the output of A₁ and A₂, along with disagreement indicators , and generates a final prediction via meta-weighted fusion:

(9)

This adaptive weighting mechanism ensures that the agent most confident about a given emotion contributes more to the final decision. The weights are further calibrated using temperature-scaled softmax over agreement scores.

To maintain representational diversity during the early phase of training and prevent premature convergence among agents sharing the same initialization, we introduce a mild diversity-preserving regularizer:

(10)

Contrary to a purely “disagreement-promoting” objective, this regularizer does not enforce arbitrary divergence. Instead, it encourages each agent to explore complementary subspaces of the emotional feature space—lexical, contextual, and meta-level—thus enriching the ensemble’s hypothesis diversity while avoiding collapse to identical solutions.

Following the diversity phase, a coordination stage is applied using a consensus alignment objective based on the Kullback–Leibler divergence:

(11)

where p_P and p_R denote the Perception and Reasoning agent distributions, respectively. This stage aligns their predictive beliefs to reach a coherent consensus while retaining complementary strengths. The resulting two-phase dynamic—initial diversity followed by consensus alignment—ensures that ensemble synergy emerges naturally through coordinated optimization rather than artificial disagreement. The total system loss is given by:

(12)

This revised formulation provides theoretical clarity for the ensemble coordination process: diversity is preserved for richer representation learning, and consensus is enforced for stable decision fusion. Together, these mechanisms underpin the interpretability and robustness of the proposed multi-agent reasoning architecture.

Overall multi-agent architecture

The proposed framework introduces a structured and cognitively inspired multi-agent architecture that decomposes fine-grained sentiment detection into three interrelated processes: perception, interpretation, and resolution. Each process is handled by a dedicated agent powered by a parameter-efficiently tuned instance of LLaMA-3.3-70B-Instruct, specialized through task-specific supervision to fulfill its designated functional role. Rather than performing sentiment classification as a single end-to-end operation, the proposed system employs a staged reasoning pipeline in which each agent contributes intermediate representations and interacts via a centralized coordination mechanism [30]. This design paradigm, inspired by decentralized cognitive control, facilitates modular optimization, transparent decision tracing, and enhanced robustness through ensemble-like agent diversity.

The model processes each input instance through three primary stages: cue extraction by the Perception Agent A_p, context-driven affective inference by the Reasoning Agent A_r, and final prediction synthesis by the Resolver Agent A_s. Fig 1 presents an overview of the complete multi-agent workflow.

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Fig 1. Overview of the multi-agent architecture.

The framework decomposes fine-grained sentiment understanding into three coordinated processes: perception, reasoning, and resolution. Each agent operates on a shared semantic substrate derived from the LLaMA backbone and contributes complementary predictions that are fused via a confidence-weighted meta-coordination mechanism.

https://doi.org/10.1371/journal.pone.0342053.g001

Formally, the system encodes the raw input x, which may include text, emojis, metadata, and optional image embeddings v, into a contextualized representation via the shared LLaMA backbone. The Perception Agent A_p computes a token-level emotion affinity matrix , where P_ij denotes the alignment between token i and emotion class j. These affinities are then transformed into normalized attention scores through a double softmax operation:

(13)

The span-level importance vector for each emotion j is obtained by aggregating contextual representations:

(14)

This matrix S acts as a shared semantic substrate upon which higher-order reasoning and resolution are performed.

The Reasoning Agent A_r receives S alongside auxiliary features —including user metadata, temporal information, and thread-level context—and produces a refined emotion score vector . Its output is computed through a gated context-attention mechanism conditioned on discourse features c, allowing the model to capture rhetorical structures, irony, and pragmatic dependencies:

(15)

where represents an emotion-specific attention head and σ is the sigmoid activation function. To mitigate overfitting and promote abstract feature generalization, we further introduce dropout-masked contrastive regularization using class prototypes :

(16)

At the final stage, the Resolver Agent A_s fuses the predictions from both A_p and A_r, denoted as and , into a final emotion distribution . This meta-fusion process is achieved through a learnable confidence-weighted aggregation scheme:

(17)

where entropy-based confidence estimators dynamically modulate the weights:

(18)

The complete system is jointly optimized under a composite loss that balances accuracy, diversity, and fusion consistency:

(19)

Model parameters are optimized using AdamW with warm-started LoRA adapters and activation checkpointing for memory efficiency. During inference, all agents operate asynchronously in parallel, enabling low-latency prediction while preserving multi-step reasoning depth. Overall, the architecture integrates pre-trained language modeling with symbolic role decomposition, achieving both interpretability and robustness through modular specialization and cooperative inference [31,32].

Agent roles

.

(1) Perception Agent.

The Perception Agent functions as the system’s perceptual interface, responsible for detecting and isolating emotionally salient cues from the raw input. It identifies localized sentiment-bearing units such as affective phrases, emojis, and stylistic markers. The agent computes an affinity tensor:

(20)

The resulting attention scores are normalized via a two-level softmax:

(21)

The weighted class representations are computed as:

(22)

To encourage sparsity and interpretability, we apply a KL-divergence regularizer that minimizes the entropy of the attention distribution for each emotion:

(23)

(2) Reasoning Agent.

The Reasoning Agent contextualizes emotion-bearing spans, incorporating pragmatic cues, sarcasm recognition, and compositional inference to derive higher-level emotional judgments:

(24)

Contrastive prototype learning is used to enhance semantic alignment:

(25)

To capture dependencies between emotion categories, we incorporate a Conditional Random Field (CRF) layer that refines joint label assignments:

(26)

(3) Disagreement Resolver Agent.

When the predictions of A_p and A_r diverge, the Resolver Agent acts as an adjudicator, merging outputs through entropy-calibrated weighting:

(27)

Fusion and diversity losses are defined as:

(28)(29)(30)

Coordination strategy

The coordination strategy serves as the overarching mechanism that integrates multiple agent predictions into a coherent, confidence-weighted decision. The final coordinated prediction is given by:

(31)

Confidence weights are derived through temperature-scaled entropy normalization:

(32)

When the disagreement exceeds a threshold τ, a meta-agent A_m is invoked to dynamically predict optimal fusion weights based on agent confidence and contextual uncertainty:

(33)

To enhance robustness, counterfactual prompting is introduced during both training and inference. For each input x, a counterfactual sample is generated by altering sentiment-bearing elements (e.g., negation reversal or emoji substitution). Consistency is enforced by minimizing the Jensen–Shannon divergence between their predictions:

(34)

where .

The total coordination objective combines supervised fusion, counterfactual consistency, and inter-agent agreement regularization:

(35)

(36)

(37)

Through this coordinated multi-agent design, the system adaptively balances local perception and global reasoning, yielding stable, context-aware, and interpretable sentiment predictions even under noisy or ambiguous input conditions.

Datasets

The effectiveness of fine-grained sentiment reasoning hinges on the diversity, difficulty, and realism of the datasets used for training and evaluation. Our experimental design leverages two complementary benchmarks: GoEmotions v2 and SemEval 2024 Task 10. Together, these corpora span a broad spectrum of affective expressions, linguistic styles, and phenomena such as sarcasm, irony, and emotion layering. Both datasets include varying degrees of multilingual content and consist of real-world, user-generated text, which strengthens ecological validity. GoEmotions is derived from Reddit, whereas SemEval Task 10 is drawn from Twitter, exhibiting distinct syntactic tendencies, community-specific usage, and topical breadth [33]. To ensure fair and reproducible comparisons, we adopt the official train/validation/test splits for each dataset and report results at both fine-grained and aggregated (coarse) levels.

The GoEmotions v2 dataset contains 58,009 Reddit comments annotated with 28 fine-grained emotion categories (e.g., joy, gratitude, sadness, embarrassment). Each instance is multi-labeled, with an average of approximately 1.3 labels per post. Formally, let denote the i-th post, and its corresponding emotion vector. The task is cast as multi-label classification , optimized with a class-reweighted binary cross-entropy (BCE) loss. To mitigate label skew, we use inverse-frequency weights , where f_k is the frequency of label k. We also construct a coarse mapping to Ekman’s six basic emotions by grouping fine labels into superclasses, enabling both flat and hierarchical evaluation.

(38)

SemEval 2024 Task 10 targets sarcasm-aware sentiment analysis on Twitter. It provides joint labels for each tweet: literal sentiment and pragmatic stance . This dual annotation facilitates analysis of emotion reversal and sarcasm-informed reasoning. In this setup, we formulate a two-head multi-task model with sentiment logits z_s and sarcasm logits z_p, trained with:

(39)

where are tunable coefficients and denotes categorical cross-entropy. Moreover, we apply sarcasm-aware attention conditioning, in which the predicted sarcasm score gates the sentiment head via a residual connection, allowing selective inversion of emotional priors when sarcasm is confidently detected [34]. Key corpus statistics are summarized in Table 1.

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Table 1. Summary of key statistical properties of the sentiment analysis datasets.

https://doi.org/10.1371/journal.pone.0342053.t001

Additionally, we quantify class imbalance using the normalized Gini/Herfindahl complement of the label distribution:

(40)

Empirically, GoEmotions yields G = 0.71 (high skew), whereas SemEval’s dual-task setup is more balanced for sentiment (G = 0.42) but less so for sarcasm (G = 0.58). This asymmetry motivates adaptive weighting and specialized objectives; in practice, we apply label-wise calibration and per-agent confidence scaling. Taken together, these datasets provide a robust testbed for assessing the generalizability and coordination efficacy of the proposed multi-agent sentiment reasoning framework.

Model efficiency analysis

To assess the computational efficiency of the proposed framework, we compare the inference time, GPU memory footprint, and floating-point operations (FLOPs) against representative baseline models under identical hardware conditions (4×A100 GPUs). As shown in Table 5, although the coordination strategy introduces additional reasoning steps, the overall overhead remains moderate (<18%) relative to GPT-4 Turbo. This indicates that multi-agent collaboration can be achieved without prohibitive computational cost.

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Table 5. Computational efficiency comparison between the proposed multi-agent framework and baseline models.

https://doi.org/10.1371/journal.pone.0342053.t005

Overall results

The performance of the multi-agent sentiment detection framework is comprehensively evaluated on two representative benchmarks, GoEmotions v2 and SemEval 2024 Task 10, emphasizing both overall classification accuracy and fine-grained emotion recognition under challenging conditions. Compared with strong baselines, including RoBERTa, DeBERTa, LLaMA-2-13B, and GPT-4 Turbo, the proposed system consistently achieves superior results across key metrics such as macro-F1, Hamming Loss, and Subset Accuracy. These findings support the central hypothesis that modular decomposition and agent-specific specialization enhance the system’s ability to capture subtle affective phenomena, including sarcasm, emotional ambiguity, and low-frequency emotion classes. The performance gains are especially notable in multi-label settings, where co-occurring emotions necessitate more sophisticated contextual reasoning.

We report detailed classification metrics aggregated over five independent runs. To ensure the statistical robustness of our findings, we additionally performed two-tailed paired t-tests across five runs for each model; results marked with an asterisk (*) indicate statistically significant improvements over the strongest baseline at p < 0.05. Let represent true positives, false positives, and false negatives for class k, and let be the set of all emotion labels. The macro-level performance is computed as:

(52)

These metrics reflect both label-level accuracy and the system’s ability to preserve consistency across multi-label outputs. As shown in Table 6, the proposed model achieves the best performance across all metrics, outperforming GPT-4 Turbo by 3.1 F1 points on SemEval and reducing Hamming Loss by 0.015 on GoEmotions. This confirms the benefit of coordinated reasoning in resolving ambiguous or conflicting cues.

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Table 6. Performance comparison on GoEmotions v2 and SemEval 2024 T10.

https://doi.org/10.1371/journal.pone.0342053.t006

To support the cross-lingual robustness and hierarchical reasoning capabilities of the proposed framework, Fig 2 presents the hierarchical structure of the emotion taxonomy and its cross-lingual applicability. The left panel illustrates the mapping from 28 fine-grained GoEmotions categories to six basic emotions, revealing clustering patterns that govern affective expression. Emotions such as gratitude, love, excitement, and pride aggregate under the joy super-category, while sadness encompasses disappointment, grief, and remorse. This hierarchy enables operation at multiple granularities, allowing the Perception Agent to capture surface cues while the Reasoning Agent contextualizes these signals within broader affective categories. The right panel extends this taxonomy to multilingual contexts (English, French, Spanish), showing alignment discovered in cross-lingual evaluation; the alignment suggests that fundamental emotional categories transcend linguistic boundaries.

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Fig 2. Hierarchical emotion taxonomy and cross-lingual mapping strategy.

(Left Panel) The hierarchical structure mapping 28 fine-grained GoEmotions categories (e.g., gratitude, grief) to Ekman’s six basic emotion supercategories (Joy, Sadness, Anger, Fear, Disgust, Surprise). (Right Panel) The cross-lingual alignment showing the preservation of these emotion mappings across English, French, and Spanish, demonstrating the system’s capability to transfer taxonomic structures across languages.

https://doi.org/10.1371/journal.pone.0342053.g002

Building on the hierarchical understanding of emotional categories, Fig 3 visualizes interdependencies uncovered by our multi-agent analysis of GoEmotions. The network representation reveals patterns of emotion co-occurrence in social-media posts, where nodes denote 28 fine-grained categories and edges reflect co-occurrence frequency and strength. Positive emotions cluster around joy, gratitude, and excitement, while negative emotions aggregate near sadness, anger, and disappointment. Community structure validates the taxonomy and supports the design of the Perception Agent, which must detect systematic patterns of co-expression. Centrality analysis reveals emotional hubs bridging different states, guiding the coordination strategy (Table 7).

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Fig 3. Network visualization of emotion co-occurrence and affective communities.

Nodes represent the 28 GoEmotions categories, with edges indicating co-occurrence strength. The graph reveals distinct clusters for positive (e.g., joy) and negative (e.g., sadness) emotions, highlighting key hubs that bridge affective states.

https://doi.org/10.1371/journal.pone.0342053.g003

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Table 7. Normalized confusion matrix on representative GoEmotions v2 classes.

https://doi.org/10.1371/journal.pone.0342053.t007

Altogether, the results affirm the viability of the coordinated multi-agent design for sentiment classification. The superior accuracy, robustness, and discriminative capability can be attributed to modular role specialization and inter-agent calibration. Subsequent sections further validate these claims through ablation and qualitative diagnostics.

Ablation study

To systematically evaluate the contribution and interaction of each component, a series of ablation experiments were conducted within the proposed multi-agent sentiment reasoning framework. Specifically, we deactivated individual agents one at a time to isolate their respective functional impacts and assess the framework’s internal dependencies. Four configurations were tested, as summarized in Table 8: (i) the complete system comprising all three agents (Perception, Reasoning, and Disagreement Resolver), (ii) a version without the Resolver Agent, (iii) a simplified model with only the Perception Agent, and (iv) a single-agent baseline based on LLaMA-3.3-70B-Instruct without multi-agent coordination. Results clearly indicate that each agent provides complementary contributions to the overall model performance, underscoring the necessity of coordinated reasoning. In particular, the Disagreement Resolver effectively mitigates false positives in ambiguous and overlapping emotional contexts, confirming its essential role in nuanced sentiment reasoning.

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Table 8. Ablation study results on SemEval T10 (mean ± std over 5 runs).

https://doi.org/10.1371/journal.pone.0342053.t008

The impact of agent configuration on F1-macro and Resolution Accuracy (RA) is presented in Table 8. RA measures the proportion of cases where conflicting predictions are correctly resolved:

(53)

where denotes the final output after resolution. Compared with models lacking reasoning or resolution mechanisms, the full system achieves an 8% improvement in RA, particularly benefiting samples containing sarcasm, irony, or multi-emotion compositions.

Further inspection reveals that the Perception Agent primarily enhances recall by broadening affective cue coverage, whereas the Reasoning Agent refines precision through contextual disambiguation of emotionally ambiguous expressions. The Disagreement Resolver acts as a confidence-weighted arbitration layer, reconciling divergent predictions while preventing the propagation of inconsistent evidence. When omitted, semantic confusions such as sadness versus disappointment and pride versus gratitude increase significantly, underscoring the necessity of explicit disagreement resolution in subjective, multi-label domains.

To assess inter-agent consistency, we compute the Disagreement Rate (DR) between Perception and Reasoning Agents:

(54)

A higher DR reflects intrinsic ambiguity within the data, emphasizing the importance of conflict resolution. As shown in Table 9, introducing the Resolver Agent reduces both DR and cross-run variance, indicating more stable convergence across training iterations.

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Table 9. Agent disagreement metrics and resolution effectiveness.

https://doi.org/10.1371/journal.pone.0342053.t009

From an information-theoretic perspective, the multi-agent decomposition can be viewed as a distributed representation-learning process that maximizes complementary subspaces of the mutual information between emotion labels E and input texts X. Each agent specializes in a distinct information subspace—surface-level affective cues for the Perception Agent and contextual-pragmatic reasoning for the Reasoning Agent—thus minimizing redundancy and improving generalization. The coordination mechanism follows a consensus-optimization objective:

where and denote agent-specific objectives, and measures divergence between predictive distributions. This formulation balances inter-agent diversity and consensus stability. Although formal convergence analysis remains future work, empirical evidence across multiple datasets indicates stable optimization behavior, reinforcing the framework’s theoretical soundness.

To further validate robustness, a sensitivity analysis was conducted on the coordination hyperparameters, including the loss weights , , (from Eq. 45) and the resolution threshold τ. As shown in Table 10, performance remains consistent across wide parameter ranges, confirming that the coordination strategy maintains stability without excessive tuning.

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Table 10. Hyperparameter sensitivity analysis of the coordination mechanism.

https://doi.org/10.1371/journal.pone.0342053.t010

Collectively, the ablation, disagreement, and sensitivity analyses demonstrate that the proposed coordination framework achieves superior accuracy, robustness, and interpretability. Each agent contributes an orthogonal competency—signal amplification, contextual reasoning, and disagreement arbitration—resulting in a cohesive system that generalizes effectively across diverse emotional distributions and linguistic contexts.

Interpretability and confidence analysis

To further investigate the internal mechanisms underpinning our framework’s decision-making process, we analyze agent-specific confidence distributions under varying text complexities. Fig 4 illustrates the confidence calibration of the three specialized agents across different emotional scenarios. For straightforward texts with explicit affective signals, all agents maintain high confidence (0.8–0.9), indicating strong consensus and stable inference. As text complexity increases—such as in sarcastic or mixed-emotion expressions—the distributions diverge, reflecting each agent’s specialized reasoning behavior. The Perception Agent sustains higher confidence in ambiguous inputs, consistent with its lexical-signal focus, whereas the Reasoning Agent exhibits broader variance, modeling contextual uncertainty more faithfully. The Disagreement Resolver dynamically adapts its confidence based on inter-agent agreement, ensuring reliable fusion even under high semantic ambiguity. This adaptive calibration confirms that multi-agent coordination not only improves accuracy but also enhances model interpretability and robustness under varying linguistic complexity. Additional qualitative examples, including domain-specific sarcasm and multi-emotion cases, are provided in the Case study section to demonstrate the model’s robustness under more linguistically complex scenarios.

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Fig 4. Confidence distributions of multi-agent sentiment analysis under varying text complexity.

The charts show high consensus on simple texts but significant divergence in complex cases. The Resolver agent dynamically calibrates final confidence to bridge the gap between lexical and contextual cues.

https://doi.org/10.1371/journal.pone.0342053.g004