A DistilBERT-based model fine-tuned for sentiment analysis on airline-related tweets. It classifies input text into positive, neutral, or negative sentiment categories.

🧠 Model Details

Model Description

This model uses distilbert-base-uncased as a base and is fine-tuned on a cleaned dataset of airline tweets. It performs multi-class classification with 3 sentiment labels.

Developed by: Nicolettem
Model type: Transformer-based (DistilBERT)
Language(s): English (en)
License: MIT
Fine-tuned from: distilbert-base-uncased

📦 Model Sources

Repository: https://huggingface.co/Nicolettem/bert-sentiment-nic
Demo Space: Gradio Demo
Dataset: Chidvi201/Twitter_Data.csv

💡 Uses

Direct Use

You can use this model to classify sentiment of customer reviews or tweets — especially in the airline or travel domain.

Downstream Use

It can serve as a base for training more domain-specific sentiment models, or be integrated into social media monitoring tools.

Out-of-Scope Use

The model was not trained on non-English tweets or formal customer service chat.
It may reflect dataset biases and perform poorly on sarcastic or ambiguous text.

⚠️ Bias, Risks, and Limitations

May reflect biases in public Tweets

Nicolettem/bert-sentiment-nic

✈️ BERT Airline Sentiment Classifier