metadata

language: en
license: apache-2.0
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
pretty_name: Airline Customer Service Dataset
tags:
  - airline
  - customer service
  - text classification
  - chatbot
  - nlp

Airline Customer Service Dataset

Dataset Description

This dataset contains customer service interactions related to the airline industry. It can be used for training and evaluating natural language processing (NLP) models for tasks such as intent classification, sentiment analysis, and topic modeling.

Homepage: [link to your project's homepage or GitHub repository (if applicable)]

Dataset Structure:

The dataset is split into two subsets:

train: Used for training your models.
test: Used for evaluating your models.

Data Fields:

Field Name	Description	Data Type
Utterance	The text of the customer's utterance or message.	string
Predicted_Intent	The predicted intent of the utterance (from a baseline model or previous labeling).	string (categorical)
Intent_Score	The confidence score associated with the predicted intent.	float
Sentiment	The sentiment expressed in the utterance (e.g., Positive, Negative).	string (categorical)
RSICs	The Reason for Seeking Information or Support (e.g., Low, Medium, High).	string (categorical)
empathy_score	A numerical score (1-5) representing the empathy level.	int
listening_score	A numerical score (1-5) representing the listening/personalization level.	int
fallback_type	The type of fallback that occurred, if any.	string (categorical)
Topic	A numerical code representing the topic of the conversation.	int
Topic_Name	A descriptive name for the topic (e.g., "General Inquiry", "Baggage Claim").	string

Data Splits:

Split	Number of Rows
train	12478
test	3120

Label Mapping (for categorical columns):

You can find the mapping of labels to numerical IDs in the label_maps dictionary within the data_loader.py file. For example: