NShreya/Comp4.0

CompSense4.0: iPhone Customer Complaint Dataset with Visual Context (Annotation-Aware Matching)

Dataset Description

CompSense4.0 is an advanced version of the customer complaint dataset that integrates visual context through image-text matching. This dataset contains iPhone customer complaints with corresponding images providing visual context.

Key Enhancement: This version introduces a sophisticated image-text matching pipeline that considers the semantic alignment of images with the conversation's textual content, its assigned aspect category, and its assigned severity level. Images are assigned only if they pass individual CLIP-based similarity thresholds across all three dimensions, and an overall combined score threshold. This ensures highly relevant visual context. Images can be reused across multiple conversations if they meet the stringent criteria.

Dataset Statistics

• Total Conversations: 915
• Conversations with Assigned Images: 914
• Conversation Assignment Rate: 99.9% (Effectively 100% in this version (target 40-50% achieved))
• Total Unique Images Scraped: 4478
• Images Never Assigned to Any Conversation: 4431

Features

• thread_id (int32): Unique identifier for each conversation thread
• text (string): Customer complaint text
• image_path (image): Associated image providing visual context (with thumbnails). None if no image met the strict matching criteria.
• aspect (string): Complaint aspect category
• severity (string): Severity level of the complaint

Aspect Categories (from original dataset)

{
  "Software": 402,
  "Software\n Quality": 226,
  "Software\nQuality": 44,
  "Hardware": 43,
  "Quality": 41,
  "Service": 40,
  "Hardware\n Quality": 30,
  "Hardware\nQuality": 8,
  "Service ": 6,
  "Software Quality": 5,
  "Software\nService": 5,
  "Price": 4,
  "\n Quality": 4,
  "Hardware \n Quality": 3,
  "Quality ": 3,
  "Software ": 3,
  "Packaging": 3,
  "Quality\nHardware": 2,
  "Quality\nSoftware": 2,
  "Packaging ": 2,
  "Service Price": 2,
  " Quality": 2,
  "Service\nPackaging": 2,
  "Price\nService": 2,
  "Hardware ": 2,
  "Price Service": 2,
  "Service Quality": 2,
  "Software\nQuality ": 2,
  "software": 1,
  "Packaging\nService": 1,
  "Quality\nService": 1,
  "Sofware": 1,
  "Quality \nSoftware": 1,
  "Software Quality ": 1,
  "Software\nService ": 1,
  "Hardware Quality": 1,
  "Packaging Sevice": 1,
  "Packaging Service ": 1,
  "Price. Service": 1,
  "Packaging Price": 1,
  "Harware. Quality": 1,
  "Service Software": 1,
  "Price Sevice": 1,
  "Software Service": 1,
  "Hardware. Service": 1,
  "Service\n Quality": 1,
  "Hardware\nPrice": 1,
  "Hardware Service": 1,
  "Service Packaging": 1,
  "Service\nPrice": 1,
  "Software\nqualtiy": 1
}

The dataset includes the following aspect categories:

• Software: App issues, iOS problems, system bugs, storage, typing
• Hardware: Physical device problems, battery, screen issues, water damage, accessories
• "Quality": Build quality, design concerns
• "Service": Customer support experiences, repair, warranty, setup
• "Price": Pricing and value concerns
• "Packaging": Packaging and delivery issues

Severity Levels (from original dataset)

{
  "Blame": 271,
  "Disapproval": 268,
  "No Explicit Reproach ": 193,
  "Accusation": 85,
  "Accusation ": 59,
  "No Explicit Reproach": 18,
  "Accusation  ": 12,
  "Blame ": 4,
  "Disapproval\nBlame": 2,
  "Disapproval\nDisapproval": 1,
  "Blame Blame": 1,
  "Disapproval Blame": 1
}

• "Blame": Direct blame towards the company
• "Disapproval": Expression of disapproval
• "Accusation": Accusatory language
• "No Explicit Reproach": Neutral complaint tone

Image Categories (as extracted from Reddit)

The dataset includes images from the following categories:

• type1_typo_I_vs_eye: Typing and keyboard issues
• type2_post_update_bug: Post-update problems
• type3_battery_drains_fast: Battery drain issues
• type4_generic_sucks: General negative feedback
• type5_screen_damage: Screen and display problems
• type6_performance_lag: Performance and speed issues
• type7_camera_problem: Camera-related issues
• type8_sound_call_quality: Audio and call quality
• type9_water_damage: Water damage incidents
• type10_charging_ports: Charging port problems
• type11_storage_full: Device storage issues
• type12_app_specific: Problems related to specific applications
• type13_connectivity: WiFi, Bluetooth, or cellular data issues
• type14_accessories: Problems with Apple accessories (AirPods, Apple Watch, chargers)
• type15_setup_activation: Issues during device setup or activation

Methodology

Image-Text Matching Process

1. CLIP Embedding: Both conversation text (enhanced with aspect/severity context) and images are embedded using CLIP (openai/clip-vit-base-patch32). Additionally, individual text prompts for each unique aspect and severity label are embedded.
2. Multi-Dimensional Similarity: For each conversation-image pair, three CLIP cosine similarity scores are calculated:
   • CLIP_Text_Image_Similarity: Between the conversation's enhanced text and the image.
   • CLIP_Aspect_Image_Similarity: Between the conversation's aspect label embedding and the image.
   • CLIP_Severity_Image_Similarity: Between the conversation's severity label embedding and the image.
3. Strict Thresholding: An image is considered a match only if all three individual similarity scores (CLIP_Text_Image_Similarity, CLIP_Aspect_Image_Similarity, CLIP_Severity_Image_Similarity) exceed their respective predefined thresholds.
4. Combined Score & Final Selection: For images passing the individual thresholds, a weighted combined score is calculated (TEXT_WEIGHT, ASPECT_WEIGHT, SEVERITY_WEIGHT). The image with the highest combined score (which also must pass an OVERALL_COMBINED_THRESHOLD) is assigned to the conversation.
5. Image Repetition: Images can be assigned to multiple conversations if they meet the criteria for each. Conversations without a suitable image (i.e., no image passed all thresholds) are left with a blank image_path.

Post-Assignment Annotation of High-Score Unassigned Images

Images that were never assigned to any conversation in the primary matching process but have a high Reddit score (e.g., upvotes) are separately analyzed. For these images, their aspect and severity are automatically inferred by finding the closest matching CLIP embedding among all pre-defined aspect and severity annotation embeddings. These inferred annotations are stored in high_score_unassigned_annotations.json.

Thresholds and Weights Used for Matching:

• CLIP_TEXT_IMAGE_THRESHOLD: 0.28

• CLIP_ASPECT_IMAGE_THRESHOLD: 0.2

• CLIP_SEVERITY_IMAGE_THRESHOLD: 0.18

• OVERALL_COMBINED_THRESHOLD: 0.25

• TEXT_WEIGHT: 0.5

• ASPECT_WEIGHT: 0.3

• SEVERITY_WEIGHT: 0.2

• High-Score Unassigned Annotation Threshold: 50

Usage

from datasets import load_dataset
import json
from huggingface_hub import hf_hub_download # Import for downloading auxiliary files

# Load the dataset
dataset = load_dataset("NShreya/Comp4.0")

# Access the main dataset split
train_data = dataset["train"]

# Example usage of main dataset
print("--- Main Dataset Samples ---")
for i, sample in enumerate(train_data):
    if i >= 5: break # Print first 5 samples
    print(f"Thread ID: {sample['thread_id']}")
    print(f"Text: {sample['text']}")
    print(f"Aspect: {sample['aspect']}")
    print(f"Severity: {sample['severity']}")
    if sample['image_path']:
        print(f"Image: {sample['image_path']}")
    print("-" * 50)

# To load additional annotation data for high-score unassigned images:
try:
    # Download the file from the Hugging Face Hub
    file_path = hf_hub_download(repo_id="NShreya/Comp4.0", filename="high_score_unassigned_annotations.json", repo_type="dataset")
    with open(file_path, 'r') as f:
        unassigned_annotations = json.load(f)
    print("\n--- Annotated High-Score Unassigned Images ---")
    for i, annotation in enumerate(unassigned_annotations):
        if i >= 5: break # Print first 5 annotated images
        print(annotation)
    if not unassigned_annotations:
        print("No high-score unassigned images were annotated.")
except Exception as e:
    print(f"\nError loading 'high_score_unassigned_annotations.json' from Hub: {e}")
    print("Please ensure process_dataset.py ran successfully and the file is present on the Hub.")


# NEW: To load assigned image scores:
try:
    # Download the file from the Hugging Face Hub
    file_path = hf_hub_download(repo_id="NShreya/Comp4.0", filename="assigned_image_scores.json", repo_type="dataset")
    with open(file_path, 'r') as f:
        assigned_scores = json.load(f)
    print("\n--- Assigned Image Scores ---")
    for i, score_entry in enumerate(assigned_scores):
        if i >= 5: break # Print first 5 score entries
        print(score_entry)
    if not assigned_scores:
        print("No assigned image scores found.")
except Exception as e:
    print(f"\nError loading 'assigned_image_scores.json' from Hub: {e}")
    print("Please ensure process_dataset.py ran successfully and the file is present on the Hub.")

Files Structure

• train/: Main dataset split
• images/: Image files referenced in image_path (only relevant images are maintained here after sync)
• thread_image_mapping.csv: Detailed mapping with all similarity scores (text, aspect, severity, BLIP, keyword)
• matching_statistics.json: Comprehensive dataset statistics and metrics for CompSense4.0
• unassigned_images.json: List of images that were never assigned to any conversation due to not meeting thresholds.
• high_score_unassigned_annotations.json: List of high-score unassigned images with inferred aspect and severity annotations.
• assigned_image_scores.json: NEW: A JSON file containing the thread_id, image_path, and individual CLIP scores (text, aspect, severity) for all images successfully assigned to a conversation. This can be useful for analysis and debugging the relevance.

Citation

If you use this dataset in your research, please cite:

@dataset{compsense4_2025,
  title={CompSense4.0: Complaint Dataset with Visual Context (Annotation-Aware Matching)},
  author={NShreya},
  year={2025},
  url={[https://huggingface.co/datasets/NShreya/Comp4.0](https://huggingface.co/datasets/NShreya/Comp4.0)}
}

License

This dataset is released under the MIT License. Please check individual image sources for specific licensing requirements.

Acknowledgments

• Images sourced from Reddit communities (r/apple, r/iphone, r/mac, r/applehelp, r/ios, r/techsupport, r/mobilerepair, r/gadgets, r/pics)
• CLIP model: OpenAI
• BLIP model: Salesforce Research
• Dataset processing: Advanced annotation-aware multi-modal matching pipeline

---

Dataset created with advanced image-text matching using CLIP model for improved semantic understanding and flexible image assignment, incorporating aspect and severity annotations for highly relevant visual context.