This model fine-tunes BERT (bert-base-uncased) to perform sentiment analysis on climate change-related tweets. It classifies tweets into four sentiment categories: anti-climate (negative), neutral, pro-climate (positive), and news.

Model Details

Model Type: Fine-tuned BERT (bert-base-uncased)
Version: 1.0.0
Framework: PyTorch & Transformers
Language: English
License: AGPL-3.0

Training Data

This model was trained on the Twitter Climate Change Sentiment Dataset, which contains tweets related to climate change labeled with sentiment categories:

news: Factual news about climate change (2)
pro: Supporting action on climate change (1)
neutral: Neutral stance on climate change (0)
anti: Skeptical about climate change claims (-1)

The dataset was cleaned with the following steps:

Features	Strategy
Hashtag	Kept
Mention	Generalized
RT Tag	Generalized
URL	Generalized
Stop Words	Kept
Special Characters	Removed

Training Procedure

Training Framework: PyTorch with Transformers
Training Approach: Fine-tuning the entire BERT model
Optimizer: AdamW with learning rate 2e-5
Batch Size: 64
Early Stopping: Yes, with patience of 3 epochs
Hardware: GPU acceleration (when available)

Model Performance

AUC-ROC

Training and Validation Loss

Limitations and Biases

The model is trained on Twitter data, which may not generalize well to other text sources.
Twitter data may contain inherent biases in how climate change is discussed.
The model might struggle with complex or nuanced sentiment expressions.
Sarcasm and figurative language may be misclassified.
The model is only trained for English language content.

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("keanteng/bert-base-generalized-climate-sentiment-wqf7007")

# Prepare text
text = "Climate change is real and we need to act now!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=1)

# Map prediction to sentiment
sentiment_map = {-1: "anti", 0: "neutral", 1: "pro", 2: "news"}
predicted_sentiment = sentiment_map[predictions.item()]
print("Predicted sentiment: " + predicted_sentiment)

Ethical Considerations

This model should be used responsibly for analyzing climate sentiment and should not be deployed in ways that might:

Amplify misinformation about climate change
Target or discriminate against specific groups
Make critical decisions without human oversight