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Emotional Valence and Arousal over Time from Ecological Essays

SemEval-2026 Task 2: predicting self-reported emotional valence and arousal from ecological, first-person diary essays written over time. Unlike third-party sentiment labels, the ratings reflect the author's own subjectively experienced affect. Annotators read a diary-style essay and rate its valence and arousal; the shared task also forecasts how affect changes across successive entries.

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Quick start:

bash
git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/semeval/2026/task02-emotion-valence-arousal-dynamics
potato start config.yaml

Configuration File

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# Emotional Valence and Arousal over Time from Ecological Essays
# Based on Soni et al., SemEval-2026 Task 2
# Paper: https://aclanthology.org/2026.semeval-1.451/
# Task page: https://semeval2026task2.github.io/SemEval-2026-Task2/
#
# Ecological essays are first-person diary entries written over time, each
# paired with the author's OWN valence/arousal self-report (subjectively
# experienced affect, not third-party sentiment). The shared task has:
#   - Subtask 1: assess affect (valence & arousal) for each essay
#   - Subtask 2A/2B: forecast future affect across successive entries
# This showcase models Subtask 1: read the essay, rate its valence and
# arousal.
#
# Valence: how pleasant/positive the writer feels (1 = very unpleasant,
#          5 = neutral, 9 = very pleasant).
# Arousal: how activated/energized the writer feels (1 = very calm/low
#          energy, 9 = very activated/high energy).

annotation_task_name: "Ecological Essay Valence & Arousal"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
export_annotation_format: "jsonl"

port: 8000

annotation_schemes:
  - annotation_type: slider
    name: valence
    description: "Valence: how pleasant/positive does the writer feel? (1 = very unpleasant, 9 = very pleasant)"
    min_value: 1
    max_value: 9
    starting_value: 5

  - annotation_type: slider
    name: arousal
    description: "Arousal: how activated/energized does the writer feel? (1 = very calm, 9 = very activated)"
    min_value: 1
    max_value: 9
    starting_value: 5

  - annotation_type: text
    name: notes
    description: "Optional notes on cues that informed your rating"
    multiline: true
    required: false

annotation_instructions: |
  Read the diary-style essay written in the first person. Rate the emotion
  the writer is expressing on two continuous dimensions: valence (how
  pleasant vs. unpleasant they feel) and arousal (how activated vs. calm
  they feel). Judge the writer's own experienced affect, not your reaction
  to the topic.

user_config:
  allow_all_users: true
max_annotations_per_user: 50
num_annotators_per_item: 3

Sample Data

json
[
  {
    "id": "eva_001",
    "text": "Today was exhausting. Back-to-back shifts, barely a break, and my feet are killing me. I just want to sleep for a week."
  },
  {
    "id": "eva_002",
    "text": "Got the promotion I've been working toward for two years! I called my mom right away and we both cried. Best day in a long time."
  }
]

// ... and 8 more items

Dataset & paper

Soni et al., SemEval 2026 (Task 2)

Citation (BibTeX)

bibtex
@inproceedings{soni-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays",
    author = "Soni, Nikita  and Schwartz, H. Andrew  and Boyd, Ryan L.  and Bui, Phi Long  and Mahwish, Syeda  and Nilsson, August H{\aa}kan  and Ganesan, Adithya V  and Ungar, Lyle  and Balasubramanian, Niranjan  and Mohammad, Saif M.",
    booktitle = "Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)",
    year = "2026",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.semeval-1.451",
    pages = "3731--3752"
}

Details

Annotation Types

slidertext

Domain

NLPEmotionSemEval

Use Cases

Emotion RecognitionDimensional AffectLongitudinal Modeling

Tags

semevalsemeval-2026shared-taskvalence-arousalemotionlongitudinal

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