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 follows the SemEval-2016 Task 5 sentence-level ABSA protocol
# (Subtask 1), which annotates three slots per opinion:
#   1. Aspect Category Detection -- an Entity#Attribute (E#A) pair from a
#      predefined inventory (e.g., FOOD#QUALITY, LAPTOP#PRICE).
#   2. Opinion Target Expression (OTE) -- the span of text naming the target
#      (marked NULL when the target is implicit).
#   3. Sentiment Polarity -- positive, negative, or neutral.
#
# Opinion Target Expression (span):
# - Highlight the exact word/phrase that names the reviewed target.
#
# Aspect Category (E#A) Labels (examples across domains):
# - RESTAURANT#GENERAL, FOOD#QUALITY, SERVICE#GENERAL, AMBIENCE#GENERAL,
#   LAPTOP#GENERAL, LAPTOP#PRICE, DISPLAY#QUALITY, BATTERY#OPERATION_PERFORMANCE,
#   HOTEL#GENERAL, ROOMS#GENERAL
#
# Sentiment Polarity Labels (sentence-level, SemEval-2016 Subtask 1):
# - Positive: Favorable opinion toward the aspect
# - Negative: Unfavorable opinion toward the aspect
# - Neutral: Mildly positive/negative or no clear polarity
# (Note: the "conflict" label used in SemEval-2014 was dropped for the
#  sentence-level 2016 subtask.)

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: opinion_target_expression
    description: "Highlight the Opinion Target Expression (OTE): the exact word or phrase naming the target being evaluated."
    labels:
      - "Opinion Target"

  - annotation_type: radio
    name: aspect_category
    description: "Select the aspect category (Entity#Attribute pair) toward which the opinion is expressed."
    labels:
      - "RESTAURANT#GENERAL"
      - "FOOD#QUALITY"
      - "SERVICE#GENERAL"
      - "AMBIENCE#GENERAL"
      - "LAPTOP#GENERAL"
      - "LAPTOP#PRICE"
      - "DISPLAY#QUALITY"
      - "BATTERY#OPERATION_PERFORMANCE"
      - "HOTEL#GENERAL"
      - "ROOMS#GENERAL"

  - annotation_type: radio
    name: sentiment_polarity
    description: "What is the sentiment expressed toward the identified aspect?"
    labels:
      - "Positive"
      - "Negative"
      - "Neutral"
    keyboard_shortcuts:
      "Positive": "1"
      "Negative": "2"
      "Neutral": "3"
    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 with only mild or no clear positive/negative sentiment"

annotation_instructions: |
  You will be shown a review sentence from a specific domain. Following the
  SemEval-2016 Task 5 sentence-level protocol, for each opinion expressed:
  1. Highlight the Opinion Target Expression (OTE) -- the exact word or phrase
     that names the target being evaluated (e.g., "battery life", "waiter").
     Leave the span empty if the target is implicit (NULL).
  2. Select the aspect category as an Entity#Attribute (E#A) pair
     (e.g., FOOD#QUALITY, LAPTOP#PRICE).
  3. Classify the sentiment polarity expressed toward it as
     positive, negative, or neutral.

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