Aspect-Based Sentiment Analysis

# Aspect-Based Sentiment Analysis
# Based on Pontiki et al., SemEval 2016
# Paper: https://aclanthology.org/S16-1002/
# Dataset: http://alt.qcri.org/semeval2016/task5/
#
# This task asks annotators to identify aspect terms in review text
# and classify the sentiment polarity expressed toward each aspect.
#
# Span Labels:
# - Aspect Term: A word or phrase representing an aspect being evaluated
#
# Sentiment Polarity Labels:
# - Positive: Favorable opinion toward the aspect
# - Negative: Unfavorable opinion toward the aspect
# - Neutral: Neither positive nor negative opinion
# - Conflict: Both positive and negative opinions about the same aspect

annotation_task_name: "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_terms
    description: "Highlight aspect terms (features or attributes being evaluated) in the review."
    labels:
      - "Aspect Term"

  - annotation_type: radio
    name: sentiment_polarity
    description: "What is the sentiment expressed toward the identified aspect?"
    labels:
      - "Positive"
      - "Negative"
      - "Neutral"
      - "Conflict"
    keyboard_shortcuts:
      "Positive": "1"
      "Negative": "2"
      "Neutral": "3"
      "Conflict": "4"
    tooltips:
      "Positive": "The reviewer expresses a favorable opinion about this aspect"
      "Negative": "The reviewer expresses an unfavorable opinion about this aspect"
      "Neutral": "The reviewer mentions the aspect without clear positive or negative sentiment"
      "Conflict": "The reviewer expresses both positive and negative opinions about this aspect"

annotation_instructions: |
  You will be shown a review text from a specific domain. Your task is to:
  1. Highlight all aspect terms -- words or phrases referring to features or attributes
     being evaluated (e.g., "battery life", "service", "taste").
  2. For the primary aspect, classify the sentiment polarity expressed toward it.

html_layout: |
  <div style="padding: 15px; max-width: 800px; margin: auto;">
    <div style="background: #fefce8; border: 1px solid #fde68a; border-radius: 8px; padding: 12px; margin-bottom: 12px;">
      <strong style="color: #a16207;">Domain:</strong>
      <span style="font-size: 15px;">{{domain}}</span>
    </div>
    <div style="background: #f0f9ff; border: 1px solid #bae6fd; border-radius: 8px; padding: 16px; margin-bottom: 16px;">
      <strong style="color: #0369a1;">Review:</strong>
      <p style="font-size: 16px; line-height: 1.7; margin: 8px 0 0 0;">{{text}}</p>
    </div>
  </div>

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