भाव विश्लेषण का कार्य बनाना

annotation_task_name: "Social Media Sentiment Analysis"
 
# Data configuration
data_files:
  - "data/tweets.json"
 
item_properties:
  id_key: id
  text_key: text
 
# Annotation interface
annotation_schemes:
  # Primary sentiment label
  - annotation_type: radio
    name: sentiment
    description: "What is the overall sentiment of this post?"
    labels:
      - name: Positive
        tooltip: "Expresses happiness, satisfaction, or approval"
        key_value: "1"
      - name: Negative
        tooltip: "Expresses sadness, frustration, or disapproval"
        key_value: "2"
      - name: Neutral
        tooltip: "Factual, objective, or lacks emotional content"
        key_value: "3"
    label_requirement:
      required: true
 
  # Confidence rating
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your sentiment label?"
    size: 5
    min_label: "Not confident"
    max_label: "Very confident"
    label_requirement:
      required: true
 
  # Optional explanation
  - annotation_type: text
    name: explanation
    description: "Why did you choose this label? (Optional)"
    rows: 4
    label_requirement:
      required: false
    placeholder: "Explain your reasoning..."
 
# Guidelines
annotation_instructions: |
  ## Sentiment Annotation Guidelines
 
  ### Your Task
  Classify the sentiment expressed in each social media post.
 
  ### Labels
 
  **Positive**: The author expresses positive emotions or opinions
  - Happiness, excitement, gratitude
  - Praise, recommendations, approval
  - Examples: "Love this!", "Best day ever!", "Highly recommend"
 
  **Negative**: The author expresses negative emotions or opinions
  - Anger, frustration, sadness
  - Complaints, criticism, disapproval
  - Examples: "Terrible service", "So disappointed", "Worst experience"
 
  **Neutral**: Factual or lacking clear sentiment
  - News, announcements, questions
  - Mixed or balanced opinions
  - Examples: "The store opens at 9am", "Has anyone tried this?"
 
  ### Tips
  - Focus on the author's sentiment, not the topic
  - Sarcasm should be labeled based on intended meaning
  - When unsure, lower your confidence rating
 
# User management
automatic_assignment:
  on: true
  sampling_strategy: random
  labels_per_instance: 1
  instance_per_annotator: 100

{"id": "t001", "text": "Just got my new laptop and I'm absolutely loving it! Best purchase of the year! #happy"}
{"id": "t002", "text": "Waited 2 hours for customer service and they still couldn't help me. Never shopping here again."}
{"id": "t003", "text": "The new coffee shop on Main Street opens tomorrow at 7am."}
{"id": "t004", "text": "This movie was okay I guess. Some good parts, some boring parts."}
{"id": "t005", "text": "Can't believe how beautiful the sunset was tonight! Nature is amazing."}

potato start config.yaml

{
  "instance_id": "t001",
  "user_id": "john_doe",
  "labels": {
    "sentiment": {"Positive": "true"},
    "confidence": {"5": "true"},
    "explanation": {"text_box": "Clear expression of happiness with the purchase"}
  },
  "spans": {},
  "links": {}
}

attention_checks:
  enabled: true
  frequency: 10          # Every 10th item
  items_file: data/attention_checks.json
  failure_handling:
    warn_threshold: 2
    block_threshold: 5

[
  {
    "id": "chk1",
    "text": "I am extremely happy and satisfied! This is the best!",
    "expected_answer": { "sentiment": "Positive" }
  },
  {
    "id": "chk2",
    "text": "This is absolutely terrible and I hate it completely.",
    "expected_answer": { "sentiment": "Negative" }
  }
]

automatic_assignment:
  on: true
  sampling_strategy: random
  labels_per_instance: 3  # Each item annotated by 3 people
  instance_per_annotator: 50

import json
from collections import Counter
 
records = []
with open('annotation_output/exports/jsonl/annotations.jsonl') as f:
    for line in f:
        records.append(json.loads(line))
 
def chosen(record, scheme):
    """The label picked for a single-choice scheme: the key whose value is "true"."""
    for name, value in record["labels"].get(scheme, {}).items():
        if value == "true":
            return name
    return None
 
sentiments = Counter(chosen(r, "sentiment") for r in records)
print(f"Sentiment distribution: {dict(sentiments)}")
 
# A likert stores its scale position the same way, as the label name
points = [int(p) for r in records if (p := chosen(r, "confidence"))]
print(f"Average confidence: {sum(points)/len(points):.2f}")