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Commonsense Emotional Event KB

Updated 14 November 2025

Commonsense Emotional Event KB is a structured, machine-readable resource that maps everyday events to prototypical human emotions using detailed templates and labels.
It employs diverse methodologies including manual curation, LLM-assisted generation, crowdsourcing, and contrastive probing to ensure high-quality, reliable annotations.
Practical applications span sentiment analysis, narrative modeling, and commonsense reasoning across languages, advancing affective computing research.

A commonsense knowledge base (KB) of emotional events is a structured, machine-readable resource that encodes events frequently associated with prototypical human emotional responses, often enriched with event templates, emotion labels, annotation schemas, reasoning roles, and, in advanced cases, appraisal dimensions or social intents. Such KBs support a wide range of computational tasks in affective computing, natural language understanding, and narrative modeling by systematically cataloguing event–emotion relations grounded in empirical data, crowdsourcing, or LLMs. Key initiatives span multiple languages and methodologies, including large-scale resources for Chinese, English, and Korean, and state-of-the-art systems for retrieval, annotation, and downstream application.

1. Core Concepts and Knowledge Base Schemas

Emotional event KBs capture mappings between everyday or literary events and the emotions they plausibly trigger. They are typically constructed around the following representational elements:

Event template: Often a phrase or clause, sometimes with argument slots (e.g., [Agent] wins a prize, PersonX drinks coffee in the morning).
Emotion label or polarity: Sentiment polarity (positive/negative), fine-grained categories (e.g., joy, sadness, anger), or free-text reactions.
Annotation schema: Structures for annotating intention, reaction, appraisal, and semantic roles of participants.
Reasoning context: Differentiation between explicit and implicit emotional cues, and role-specific inferences (e.g., agent vs. recipient).
Multilinguality and cultural scope: Resources are now available in Chinese, English, and Korean, each addressing specific linguistic or cultural features.

Several prominent KBs embody these principles:

KB/Corpus	Language	Entries	Key Schema Fields / Label Sets
Chinese Emotional-Events KB (Wang et al., 7 Nov 2025)	Chinese	102,218	{indicator, theme, polarity, source_type}
Event2Mind (Rashkin et al., 2018)	English	~25,000 event templates	{event_template, x_intents, x_reacts, o_reacts}
K-Act2Emo (Kim et al., 21 Mar 2024)	Korean	1,900 heads, 6,002 tails	(Head, Relation, Tail): PosEnv/NegEnv/NonEmo
Modeling Naive Psychology (Rashkin et al., 2018)	English	15,000 stories	Story events, motivations (Maslow/Reiss), reactions (Plutchik)
x-enVENT (Troiano et al., 2022)	English	929 sentences	(event_span, experiencer, emotion_label, appraisal_vector)

Event descriptions can be explicit—containing overt emotion words—or implicit, requiring commonsense reasoning to connect neutral phrasing to an emotion (Hu et al., 24 Oct 2024).

2. Methodologies for Resource Construction

Construction techniques reflect both linguistic and computational advances and differ across KBs:

Manual Indicator and Event Collection (Wang et al., 7 Nov 2025, Kim et al., 21 Mar 2024) Indicator phrases or indirect cues are curated through literature review, lexical resources, and manual expansion, creating a foundation of event patterns. In K-Act2Emo, indirect emotional expressions are contributed by expert writers using seeding strategies and rigorous deduplication.
LLM-Based Generation and Filtering (Wang et al., 7 Nov 2025, Hu et al., 24 Oct 2024) For large-scale coverage, prompts are fed to LLMs (e.g., SparkDesk for Chinese) using in-context examples to elicit event phrases for each indicator. Generated events undergo posthoc validation via supervised classifiers (e.g., RoBERTa-Large) trained on manually labeled data. Loss function applied is:

$\mathcal{L}_{CE} = -\frac{1}{N} \sum_{i=1}^{N} \Bigl[ y_i \log \hat y_i + (1-y_i) \log (1-\hat y_i) \Bigr]$

Crowdsourced Annotation (Rashkin et al., 2018, Troiano et al., 2022) Workers on Mechanical Turk or trained annotators label events with agency, intent, emotional reaction, and appraisal dimensions, facilitating fine-grained, multi-perspective labels.
Contrastive Probing for Event Retrieval (Hu et al., 24 Oct 2024) Supervised contrastive losses guide a probe to align emotion prompt embeddings with event phrase embeddings, maximizing within-class similarity while minimizing between-class similarity:

$L = \sum_{i=1}^N \left[ -\frac{1}{|P(i)|} \sum_{p \in P(i)} \log \frac{\exp(\mathrm{sim}(z_i, z_p)/\tau)}{\sum_{a \in A(i)} \exp(\mathrm{sim}(z_i, z_a)/\tau)} \right]$

where $\mathrm{sim}(z_i, z_j)$ is a normalized inner product between projected embeddings.

Inter-annotator Agreement and Curation Agreement scores (e.g., Cohen’s $\kappa$ , Fleiss’s $\kappa$ ) and Likert validation screens ensure annotation reliability, with high $\kappa$ scores observed in Chinese event KBs (0.95 for non-neutral, 0.92 for “bei” events) (Wang et al., 7 Nov 2025), and 0.84 for experiencer span detection in x-enVENT (Troiano et al., 2022).

3. Intrinsic and Extrinsic Evaluations

Intrinsic evaluation assesses the KB's quality using precision, inter-annotator agreement, and acceptance rates:

Chinese Emotional Events KB (Wang et al., 7 Nov 2025)
- Precision: 0.96 (non-neutral events), 0.94 (“bei” events).
- Coverage: 102,218 high-quality Chinese events, explicit polarity.
- Fleiss’s $\kappa$ : 0.95/0.92 (non-neutral/“bei”).
K-Act2Emo (Kim et al., 21 Mar 2024)
- Acceptance rate: 82.53% of inferences validated.
- Human Accept %: COMET-BART 97.2%, GPT-4 Turbo 95.4%.
x-enVENT (Troiano et al., 2022)
- Inter-annotator agreement (emotion labels): $\kappa=0.62$ (overall), up to 0.84 (joy).
- Appraisal rating: up to $\kappa=0.88$ , $\rho=0.79$ (self-responsibility).

Extrinsic evaluations demonstrate utility in downstream tasks:

Emotion Cause Extraction (ECE) (Wang et al., 7 Nov 2025)
- Augmenting ECE models with indicator-based features produced systematic F₁ improvements across eight architectures ( $+1.29$ to $+1.49$ ), confirming practical benefit.
Event2Mind (Rashkin et al., 2018)
- Recall@10: ~40% (intent, XReact), ~67% (OReact).
- Sequence decoders with BiRNN encoders outperform pooling/convnet alternatives.
K-Act2Emo Models (Kim et al., 21 Mar 2024)
- COMET-BART trained on K-Act2Emo achieves BLEU-4 0.259, ROUGE-L 0.566, KoBERTScore 0.770, surpassing all native Korean LLMs tested and matching one-shot GPT-4 Turbo.
Contrastive Retrieval (Hu et al., 24 Oct 2024)
- Joy events: High precision but low diversity; Sadness and Angry retrieval much weaker, highlighting event/lexicon imbalances in both resource and model.

4. Knowledge Base Coverage, Structure, and Representation

Representation formats are tailored for both human interpretability and algorithmic access:

Chinese Emotional-Events KB (Wang et al., 7 Nov 2025) JSON records:

1	{ "indicator": "获得", "theme": "国际大奖", "polarity": +1, "source_type": "classic" }

Event2Mind (Rashkin et al., 2018) CSV/JSON with fields: event_template, x_intents, x_reacts, o_reacts.
K-Act2Emo (Kim et al., 21 Mar 2024) Triples (Head, Relation, Tail), where relations are {PosEnv, NegEnv, NonEmo}.

x-enVENT (Troiano et al., 2022) Frame format:

{
  "id": "EV123",
  "event_span": "shouted at me in front of my colleagues",
  "participants": [
    { "role": "writer", "emotion": "disgust", "appraisal": {...} },
    { "role": "colleague", "emotion": "sadness", "appraisal": {...} }
  ]
}

KBs support querying by event, polarity, role, or appraisal vector, providing flexible access modalities.

Coverage differentiates these resources from prior art:

Resource	Language	Size/Main Focus
ConceptNet (Chinese)	Chinese	2,571 event nodes (1% emotional)
EveSA (FrameNet)	English	18 event types, ~1,500 sentences
SemEval-2015 Task 9	English	1,651 event instantiations
Chinese Emotional-Events KB	Chinese	102,218 explicit-polarity events
Event2Mind	English	~25,000 templates x 3 annotations
K-Act2Emo	Korean	1,900 heads, 6,002 tails
Modeling Naive Psychology	English	15,000 stories x 300K annotations
x-enVENT	English	912 events, 1,329 experiencer annotations

5. Applications, Impact, and Limitations

Emotional-event KBs unlock numerous capabilities:

Emotion Cause Extraction (ECE): Detecting the text span responsible for an expressed emotion is substantially enhanced by KB features (Wang et al., 7 Nov 2025).
Implicit Sentiment Analysis: Identifying affective cues when no emotion terms are present (e.g., “win a scholarship”) (Wang et al., 7 Nov 2025).
Dialogue and Narrative Modeling: Enabling affect-aware systems to anticipate, track, or simulate changes in user or character affect (Rashkin et al., 2018, Rashkin et al., 2018, Kim et al., 21 Mar 2024).
Commonsense QA and Reasoning: Integrating event–emotion inferences as relations in knowledge graphs improves reasoning workflows (Wang et al., 7 Nov 2025, Kim et al., 21 Mar 2024).
Multilingual and Cultural Expansion: Extending KBs to Korean (Kim et al., 21 Mar 2024) and Chinese (Wang et al., 7 Nov 2025) addresses documented coverage gaps in resources such as ATOMIC2020.

Documented limitations and challenges include:

Coverage Imbalance: Existing resources like ConceptNet or ATOMIC2020 underrepresent emotional events (1% in Chinese ConceptNet; only ~161 of 1,707 K-Act2Emo heads in ATOMIC2020) (Kim et al., 21 Mar 2024).
Role and Perspective Ambiguity: Most KBs associate events with agent-centric or surface roles; even multi-role corpora like x-enVENT exhibit co-occurrence but not full causal modeling (Troiano et al., 2022).
Implicit Event Bias: LLMs and retrieval approaches are biased toward explicit-event retrieval; implicit triggers often demand human-in-the-loop iteration and template augmentation (Hu et al., 24 Oct 2024).
Emotion Taxonomy Granularity: Fine distinctions such as high-arousal anger vs. fear and observer bias remain open technical issues (Kim et al., 21 Mar 2024).
Scalability of Manual Appraisal: Deep appraisal-theoretic annotation is high-fidelity but slow to scale, yielding corpora like x-enVENT with only 912 event spans (Troiano et al., 2022).

6. Directions for Resource Expansion and Best Practices

Recent findings emphasize the following strategies for robust emotional-event KB construction (Hu et al., 24 Oct 2024):

Seed lists: Ensure balanced, culturally sensitive, and context-independent event seeds for each emotion category, balancing explicit and implicit triggers.
LLM-assisted retrieval with contrastive ranking: Apply supervised contrastive probing to filter high-confidence event–emotion pairs; measure both P@K (precision) and D@K (diversity).
Iterative, human-in-the-loop curation: Regularly verify and deduplicate outputs, correcting LLM bias and addressing underrepresented categories.
Taxonomy expansion: Move beyond triadic or binary polarity to hierarchical emotion ontologies.
Augmentation via template roles: Systematically expand argument slots and roles to capture generic and specific instantiations.
Cross-lingual benchmarking: Translate and map across KBs to identify coverage gaps (e.g., only 9.4% K-Act2Emo heads in ATOMIC2020) (Kim et al., 21 Mar 2024).

Potential pitfalls—such as model overreliance on lexical cues, low diversity in difficult categories, and propagation of templatic errors without human oversight—are widely acknowledged. Incorporating ensemble solutions and template-based data synthesis has been recommended for diversity and recall improvements, particularly for emotions with weak explicit lexicalization.

7. Comparative Landscape and Significance

The emergence of large, high-precision, multicultural emotional-event KBs represents a substantive advance in affective commonsense AI. The Chinese KB (Wang et al., 7 Nov 2025) delivers two orders of magnitude greater coverage of generalized, context-independent emotional events than prior multilingual resources. Event2Mind (Rashkin et al., 2018), Modeling Naive Psychology (Rashkin et al., 2018), and x-enVENT (Troiano et al., 2022) furnish deep, role-specific and appraisal-theoretic views for English, addressing narrative and self-report contexts. K-Act2Emo (Kim et al., 21 Mar 2024) uniquely fills a gap in indirect emotional expression in Korean, formalizing valenced reasoning under positive, negative, or non-emotional contexts. Supervised contrastive retrieval and human-guided iterative expansion (Hu et al., 24 Oct 2024) anchor current best practices for constructing balanced, application-ready emotional event knowledge bases.

A plausible implication is that the continued systematic expansion and integration of these resources—across languages, emotional ontologies, annotation schemas, and technological pipelines—will underpin the next generation of affect-aware machine reasoning, narrative modeling, and human-centric NLP systems.