Datasets:

rootfs
/

user-satisfaction-dataset

license: apache-2.0
language:
  - en
task_categories:
  - text-classification
tags:
  - conversational-ai
  - user-satisfaction
  - dissatisfaction
  - feedback-classification
size_categories:
  - 10K<n<100K

User Satisfaction Classification Dataset

A dataset for training classifiers to detect user satisfaction and dissatisfaction reasons from follow-up messages in conversational AI systems.

Dataset Description

This dataset contains user follow-up messages labeled with satisfaction status and dissatisfaction reasons. The key insight is that follow-up messages alone contain sufficient signal for classification—no conversation context is needed.

Labels

Label	Description	Count (Train)
`SAT`	User is satisfied	~3,350
`NEED_CLARIFICATION`	User needs more explanation	~3,350
`WRONG_ANSWER`	System provided incorrect information	~3,350
`WANT_DIFFERENT`	User wants alternative options	~3,350

Dataset Structure

train.jsonl          # 13,422 training examples
val.jsonl            # 1,494 validation examples  
adversarial_test.jsonl # 103 adversarial test examples

Data Format

Each line is a JSON object:

{"text": "What do you mean by that?", "label_name": "NEED_CLARIFICATION"}
{"text": "Thanks, that's perfect!", "label_name": "SAT"}
{"text": "No, that's incorrect", "label_name": "WRONG_ANSWER"}
{"text": "Show me other options", "label_name": "WANT_DIFFERENT"}

Usage

from datasets import load_dataset

dataset = load_dataset("rootfs/user-satisfaction-dataset")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
print(train[0])
# {'text': 'What do you mean?', 'label_name': 'NEED_CLARIFICATION'}

Data Quality

Balanced classes: Equal representation across all 4 labels
Normalized text: 15-100 characters, no format shortcuts
100% unique: No duplicate examples
Adversarial test set: Manually curated challenging examples

Source

Derived from public conversational AI datasets:

MIMICS (Microsoft)
SGD (Google)
INSCIT

Associated Model

Pre-trained classifier: rootfs/dissat-4class

Citation

@dataset{user_satisfaction_2024,
  title={User Satisfaction Classification Dataset},
  author={Semantic Router Project},
  year={2024},
  url={https://huggingface.co/datasets/rootfs/user-satisfaction-dataset}
}

License

Apache 2.0