Aspect-Based Sentiment Analysis (Original ABSA)

# Original ABSA - Aspect-Based Sentiment Analysis
# Based on Pontiki et al., SemEval 2014
# Paper: https://aclanthology.org/S14-2004/
# Dataset: https://alt.qcri.org/semeval2014/task4/
#
# This task requires annotators to identify aspect terms in review text
# and classify the sentiment polarity expressed toward each aspect.
# The task covers two domains: restaurant reviews and laptop reviews.
#
# Aspect terms are specific features or attributes mentioned in the review
# (e.g., "battery life", "screen", "pasta", "service").
#
# Sentiment Labels:
# - Positive: The opinion toward the aspect is favorable
# - Negative: The opinion toward the aspect is unfavorable
# - Neutral: The aspect is mentioned factually without sentiment
# - Conflict: Both positive and negative sentiments are expressed

annotation_task_name: "Original ABSA - Aspect-Based Sentiment Analysis"
task_dir: "."

data_files:
  - sample-data.json

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

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

port: 8000
server_name: localhost

annotation_schemes:
  - annotation_type: span
    name: aspect_term_spans
    description: "Highlight all aspect terms (specific features or attributes) mentioned in the review."
    labels:
      - "Aspect Term"
    tooltips:
      "Aspect Term": "A specific feature, attribute, or entity being evaluated (e.g., battery life, pasta, service, screen resolution)"

  - annotation_type: radio
    name: sentiment_polarity
    description: "What is the overall sentiment polarity of this review toward the identified aspects?"
    labels:
      - "Positive"
      - "Negative"
      - "Neutral"
      - "Conflict"
    keyboard_shortcuts:
      "Positive": "1"
      "Negative": "2"
      "Neutral": "3"
      "Conflict": "4"
    tooltips:
      "Positive": "The opinion expressed toward the aspect is clearly favorable or positive"
      "Negative": "The opinion expressed toward the aspect is clearly unfavorable or negative"
      "Neutral": "The aspect is mentioned factually without expressing positive or negative sentiment"
      "Conflict": "Both positive and negative sentiments are expressed toward the aspect"

annotation_instructions: |
  You will be shown review text from the restaurant or laptop domain.

  **Step 1: Aspect Term Identification**
  Highlight all aspect terms in the text. An aspect term is a specific
  feature, attribute, or entity being discussed or evaluated. Examples:
  - Restaurant: food, service, ambiance, price, waiter, pizza, dessert menu
  - Laptop: battery life, screen, keyboard, trackpad, processor, fan noise

  **Step 2: Sentiment Classification**
  Classify the overall sentiment polarity:
  - **Positive**: Favorable opinion (e.g., "excellent sushi", "fast boot time")
  - **Negative**: Unfavorable opinion (e.g., "slow service", "cheap trackpad")
  - **Neutral**: Factual mention without sentiment (e.g., "the menu has 10 items")
  - **Conflict**: Mixed sentiments (e.g., "the food is great but overpriced")

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #eff6ff; border: 1px solid #93c5fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #1e40af;">Review Text:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
    <div style="background: #f5f3ff; border: 1px solid #c4b5fd; border-radius: 8px; padding: 12px; margin-bottom: 16px;">
      <strong style="color: #5b21b6;">Domain:</strong>
      <span style="font-size: 15px; margin-left: 8px; text-transform: capitalize;">{{domain}}</span>
    </div>
  </div>

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false