cs.CLApr 10, 2026
SavePersona-E: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events
Organizations: School of Future Technology, South China University of Technology, Guangzhou, China
Abstract
Most affective computing research treats emotion as a static property of text, focusing on the writer's sentiment while overlooking the reader's perspective. This approach ignores how individual personalities lead to diverse emotional appraisals of the same event. Although role-playing Large Language Models (LLMs) attempt to simulate such nuanced reactions, they often suffer from "personality illusion'' -- relying on surface-level stereotypes rather than authentic cognitive logic. A critical bottleneck is the absence of ground-truth human data to link personality traits to emotional shifts. To bridge the gap, we introduce Persona-E (Persona-Event2Emotion), a large-scale dataset grounded in annotated MBTI and Big Five traits to capture reader-based emotional variations across news, social media, and life narratives. Extensive experiments reveal that state-of-the-art LLMs struggle to capture precise appraisal shifts, particularly in social media domains. Crucially, we find that personality information significantly improves comprehension, with the Big Five traits alleviating "personality illusion.'
Figures & tables
Figure 1: Overview of the Persona-E 2 framework. Events from three domains undergo multi-stage data processing. High-quality stimuli are then annotated by a Persona Group, serving to evaluate three research questions.
| Dataset | Year | #Events | #Annotations | Perspective | #Emotions | Personality |
| News-based Domain | ||||||
| GoodNewsEveryone Bostan et al. (2020) | 2020 | 5,000 | 15,000 | Writer + Reader | 15 | ✗ |
| NewsMTSC Hamborg and Donnay (2021) | 2021 | 11,029 | 56,000 | Writer | 7 | ✗ |
| iNews Hu and Collier (2025) | 2025 | 2,899 | 14,550 | Reader | 6 | ✗ |
| Social Media Domain | ||||||
| SemEval-2018 Task 1 Mohammad et al. (2018) | 2018 | 22,000 | 700,000 | Writer | 4 | ✗ |
Table 1: A unified comparison of emotion-annotated datasets across three sources: News, Social Media, and Life Experience. Note: OV: Open vocabulary.
Figure 2: Hierarchical composition of the Persona-E 2 dataset. The sunburst chart illustrates three levels from inner to outer: original data sources, three primary domains, and fine-grained semantic subcategories. Note: Text Length: Short (0–30), Medium (30–100), Long (100–300).
Figure 3: Comparison of emotion distributions between Writers and Readers showing the percentage distribution of seven emotions across sources . Note: IUTB, ASK stand for r/iusedtobelieve and RandomActsofKindness.
Figure 4: Top- emotion distribution across BFI clusters. The heatmap shows the average vote share (%) of the -th most frequent emotion per event.
Table 2: Personality-based Top-1 agreement analysis. Top: Comparison of In-Group ( ) and Out-Group ( ) agreement levels ( controlled). Bottom: Mean BFI traits for clusters. Note: Open., Cons., Extra., Agree., and Neuro. represent the Big Five traits; denotes the number of annotators.
Figure 5: Affective transition matrices between General Writer and Reader. Red boxes highlight: (a) high resonance in News, (b) negative bias in Social Media, and (c) positive shift in Life Experience. Note: Negative: disgust, fear, anger, sadness; Positive: surprise, joy.
Figure 6: (a) Transfer rate from non-negativity to negativity in the news domain, and (b) Transfer rate from negativity to non-negativity, error bar means deviation among 3 domains.
| Metric | Source | News | Social Media | Life Experience | Overall | ||||||||
| Prompt | General | Persona | CoT | General | Persona | CoT | General | Persona | CoT | General | Persona | CoT | |
| Top-1 Acc. | GPT5.1 | 31.8 | 25.0 | 29.5 | 18.2 | 18.2 | 27.3 | 32.4 | 35.3 | 35.3 | 29.0 | 27.0 | 31.0 |
| Llama3-8B | 29.5 | 29.3 | 20.5 | 9.1 | 4.8 | 13.6 | 32.4 | 32.4 | 32.4 | 26.0 | 25.0 | 23.0 | |
| Qwen3-8B | 18.2 | 26.9 | 25.4 | 23.1 | 21.4 | 22.7 | 20.3 | 33.7 | 26.0 | 20.0 | 28.0 | 25.0 | |
| Gemma3-12B | 18.2 | 25.0 | 27.3 | 22.7 | 27.3 | 18.2 | 35.3 | 32.4 | 29.4 | 25.0 | 28.0 | 26.0 | |
| Ministral3-8B | 18.2 | 13.6 | 20.5 | 5.0 | 9.1 | 4.5 | 36.4 | 35.3 | 35.3 | 22.0 | 20.0 | 22.0 | |
Table 3: Comparison of different models performance in subset. Note: General Prompt: Prompt without personality traits, Persona: Prompt with BFI traits, CoT: CoT-Style Prompt with BFI traits.
| Model | Consistency | Plausibility | Specificity | ||||||
| BL | MBTI | BFI | BL | MBTI | BFI | BL | MBTI | BFI | |
| GPT5.1 | 4.7 | 16.5 | 78.8 | 13.8 | 15.8 | 70.4 | 17.6 | 13.5 | 68.9 |
| Llama3 | 9.4 | 19.8 | 70.8 | 21.5 | 23.1 | 55.4 | 20.5 | 18.2 | 61.3 |
| Qwen3 | 10.4 | 19.4 | 70.2 | 19.7 | 20.4 | 59.9 | 23.7 | 19.4 | 56.9 |
| Gemma3 | 14.8 | 17.4 | 67.8 | 21.8 | 15.1 | 63.1 | 19.5 | 15.5 | 65.0 |
| Ministral3 | 12.5 | 15.5 | 72.0 | 21.6 | 19.5 | 58.9 | 22.0 | 12.5 | 65.5 |
Table 4: Win-Rate comparison of LLMs in the best-of-3 selection task. Note: BL: Baseline Prompt without personality, MBTI: MBTI Prompt, BFI: BFI Prompt.
Appendix figures & tables11 assets
Supplementary material from the paper’s appendix.
Appendix
| Domain | Source | Cutoff | Count |
| News | ABC_news | 3.5 | 130 |
| BBC_news | 3.5 | 248 | |
| Independent | 3.5 | 36 | |
| Today | 4.0 | 314 | |
| The Paper | 4.0 | 273 | |
| 4.0 | 478 |
Table 5: Source Distribution & Source-dependent Filtration Thresholds. Note: Independent (Independent News), Today (Today’s Headlines), SocialChem (SocialChemistry), B.E. (BenignExistence), KindLfe (RandomActsofKindness).
Figure 7: The annotation platform enables emotion labeling with seven categories, supporting live visualization, real-time data transmission, and comprehensive annotation monitoring.
| ID | GNDR | MBTI | Open. | Cons. | Extra. | Agree. | Neuro. |
| E1 | M | INTP | 0.698 | 0.646 | 0.604 | 0.740 | 0.417 |
| E2 | M | INFP | 0.615 | 0.354 | 0.479 | 0.552 | 0.677 |
| E3 | F | INFP | 0.760 | 0.458 | 0.427 | 0.688 | 0.635 |
| E4 | M | ISTJ | 0.500 | 0.802 | 0.500 | 0.729 | 0.406 |
| E5 | M | INTJ | 0.688 | 0.792 | 0.510 | 0.781 | 0.312 |
| E6 | F | ISTJ | 0.573 | 0.625 | 0.562 | 0.750 | 0.427 |
Table 6: Demographic and Personality Profiles of Participants. Annotators are indexed. BFI scores correspond to Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism .
| Method | k=3 | k=4 | k=5 | k=6 | k=7 | k=8 | k=9 |
| K-Means | 10.45 | 13.45 | 13.92 | 16.44 | 18.10 | 20.08 | 23.10 |
| GMM | 12.00 | 12.95 | 14.85 | 18.70 | 20.29 | 19.86 | 22.24 |
| Hierarchical | 10.07 | 13.87 | 15.26 | 17.35 | 17.42 | 18.57 | 19.72 |
Table 7: Sensitivity analysis of the Personality Agreement Gap (PAG) across different clustering methods and cluster counts ( ). All values represent the average Top-1 agreement gain (%) over the global baseline.
| News | |||
| Writer Reader | Positive | Neutral | Negative |
| Positive | 48.10 | 26.30 | 25.60 |
| Neutral | 28.72 | 43.66 | 27.62 |
| Negative | 21.07 | 22.33 | 56.60 |
Table 8: Affective polarity transition matrices between General Writer and General Reader in news domain.
| Social Media | |||
| Writer Reader | Positive | Neutral | Negative |
| Positive | 22.25 | 18.40 | 59.35 |
| Neutral | 4.70 | 13.70 | 81.60 |
| Negative | 11.78 | 15.35 | 72.87 |
Table 9: Affective polarity transition matrices between General Writer and General Reader in social media domain.
| Life Experience | |||
| Writer Reader | Positive | Neutral | Negative |
| Positive | 62.40 | 5.55 | 32.05 |
| Neutral | 56.20 | 13.00 | 30.80 |
| Negative | 43.28 | 6.65 | 50.07 |
Table 10: Affective polarity transition matrices between General Writer and General Reader in life experience domain.
| Start Target | Positive | Neutral | Negative |
| Cluster 1: High A, High N | |||
| Positive | 45.45% | 17.17% | 37.37% |
| Neutral | 11.72% | 36.72% | 51.56% |
| Negative | 3.45% | 5.71% | 90.84% |
| Cluster 3: High A, Low N | |||
| Positive | 66.67% | 14.14% | 19.19% |
Table 11: Polarity Transfer Matrix for different clusters in the Social domain. Note: Grouped by cluster types. A: Agreement, N: Neuroticism.
| Start Target | Positive | Neutral | Negative |
| Cluster 0: High C, High E | |||
| Positive | 66.57% | 15.88% | 17.55% |
| Neutral | 27.98% | 53.28% | 18.73% |
| Negative | 12.55% | 14.10% | 73.34% |
| Cluster 2: Lower C | |||
| Positive | 82.73% | 3.62% | 13.65% |
Table 12: Polarity Transfer Matrix for different clusters in the News domain. Note: Grouped by cluster types. C: Conscientiousness, E: Extraversion.
| Metric | C0 | C1 | C2 | C3 | C4 | C5 |
| Open. | 0.823 | 0.718 | 0.553 | 0.695 | 0.556 | 0.612 |
| Neu. Rate | 88.21% | 64.28% | 23.92% | 58.72% | 33.19% | 70.75% |
Table 13: Comparison of Openness scores and Neutralization Rates across different clusters.
| Metric | News | Social Media | Life Experience | Overall | ||||||||
| General | Persona | CoT | General | Persona | CoT | General | Persona | CoT | General | Persona | CoT | |
| Top-1 Accuracy | 34.0 | 32.0 | 34.0 | 37.1 | 37.1 | 34.3 | 33.3 | 46.7 | 40.0 | 35.0 | 36.0 | 35.0 |
| Top-2 Accuracy | 62.0 | 60.0 | 58.0 | 48.6 | 45.7 | 45.7 | 53.3 | 53.3 | 46.7 | 56.0 | 54.0 | 52.0 |
Table 14: Performance of GPT5.1 on the randomly selected set from the dataset.
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