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NRC-VAD: Valence, Arousal & Dominance via Best-Worst Scaling

Rate individual English words on three affective dimensions - valence (pleasant-unpleasant), arousal (active-calm), and dominance (in control-controlled) - using Best-Worst Scaling. Annotators see a 4-tuple of words and pick the highest and lowest word for each dimension; real-valued scores are derived from best-minus-worst counts. This is the annotation design behind the NRC-VAD Lexicon of 20,000+ words (Mohammad, ACL 2018).

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Configuration Fileconfig.yaml

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

yaml
# NRC-VAD: Valence, Arousal & Dominance via Best-Worst Scaling
# Based on Mohammad (ACL 2018), "Obtaining Reliable Human Ratings of
# Valence, Arousal, and Dominance for 20,000 English Words."
#
# Best-Worst Scaling (BWS / MaxDiff) annotation design:
#   - Annotators are shown a set (tuple) of four words.
#   - For each affective dimension they pick the ONE word that is highest
#     and the ONE word that is lowest on that dimension.
#   - Each word appears in many different 4-tuples across the study.
#   - A real-valued score in [0, 1] for each word is later computed as the
#     proportion of times it was chosen best minus the proportion of times
#     it was chosen worst. BWS yields far more reliable, consistent scores
#     than asking annotators for absolute ratings on a numeric scale.
#
# The three dimensions (Osgood's semantic differential / Russell's model):
#   - Valence:   pleasure - displeasure (happy vs. sad / positive vs. negative)
#   - Arousal:   active - passive (excited/alert vs. calm/sluggish)
#   - Dominance: dominant - submissive (in control vs. being controlled)
#
# Annotation guidelines:
#   1. Judge the word's typical, out-of-context affective association.
#   2. Answer all three dimensions independently - a word can be high on
#      one dimension and low on another (e.g., "terrorist" is low valence
#      but high arousal and high dominance).
#   3. The best and worst word may differ across the three dimensions.
#   4. If a word is unfamiliar, use the Skip button rather than guessing.

annotation_task_name: "NRC-VAD Best-Worst Scaling"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "prompt"
  text_display_key: "prompt"
  list_display_key: "options"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  # ---- Valence ----
  - annotation_type: radio
    name: valence_best
    description: "VALENCE - which word is the MOST positive / pleasant?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

  - annotation_type: radio
    name: valence_worst
    description: "VALENCE - which word is the MOST negative / unpleasant?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

  # ---- Arousal ----
  - annotation_type: radio
    name: arousal_best
    description: "AROUSAL - which word is the MOST active / excited / alert?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

  - annotation_type: radio
    name: arousal_worst
    description: "AROUSAL - which word is the MOST calm / passive / sluggish?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

  # ---- Dominance ----
  - annotation_type: radio
    name: dominance_best
    description: "DOMINANCE - which word conveys the MOST control / power?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

  - annotation_type: radio
    name: dominance_worst
    description: "DOMINANCE - which word conveys the LEAST control (most controlled / weak)?"
    labels:
      - "Word 1"
      - "Word 2"
      - "Word 3"
      - "Word 4"

allow_all_users: true
instances_per_annotator: 100
annotation_per_instance: 4
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

json
[
  {
    "id": "vad_001",
    "prompt": "Word 1: love | Word 2: coffin | Word 3: chair | Word 4: riot",
    "options": [
      "love",
      "coffin",
      "chair",
      "riot"
    ]
  },
  {
    "id": "vad_002",
    "prompt": "Word 1: serene | Word 2: panic | Word 3: cardboard | Word 4: victory",
    "options": [
      "serene",
      "panic",
      "cardboard",
      "victory"
    ]
  }
]

// ... and 8 more items

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Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/text/emotion-sentiment/nrc-vad-bws
potato start config.yaml

Dataset & paper

Mohammad, ACL 2018

Citation (BibTeX)

bibtex
@inproceedings{mohammad-2018-obtaining,
    title = "Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 {E}nglish Words",
    author = "Mohammad, Saif",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1017",
    pages = "174--184"
}

Details

Annotation Types

radio

Domain

NLPAffective ComputingLexicon Building

Use Cases

Valence-Arousal-DominanceLexicon BuildingBest-Worst Scaling

Tags

best-worst-scalingmaxdiffvalence-arousal-dominancevadnrc-vadword-levelaffectmohammadacl2018

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