airline-sentiment-models

Airline Passenger Sentiment Models

Fine-tuned transformer checkpoints from the M.Sc. thesis "Multi-Source Natural Language Processing for Airline Passenger Sentiment and Satisfaction Analysis" (Enes Gerem, Hacettepe University).

Each checkpoint is a 3-class sentiment classifier (negative / neutral / positive) fine-tuned on one of seven source configurations drawn from Twitter, Skytrax, and TripAdvisor airline feedback.

Label mapping

Available checkpoints (subfolders)

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

repo = "enesgerem/airline-sentiment-models"
subfolder = "roberta/twitter"   # pick any subfolder from the table above

tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=subfolder)
model = AutoModelForSequenceClassification.from_pretrained(repo, subfolder=subfolder)

Citation / data

Datasets are used for non-commercial academic research. The Twitter component is the Crowdflower US Airline Sentiment benchmark (CC BY-NC-SA); the Skytrax and TripAdvisor components are point-in-time scrapes of publicly available review pages, used under each platform's terms of service.