Classificador de Sentiments a Xarxes Socials en Català (CSXSC)
This model is a fine-tuned version of
projecte-aina/roberta-base-ca-2 for
three-class sentiment analysis (positive, negative, neutral) of Catalan-language texts from social media and reviews.
Developed as part of a Master's Thesis, the
CSXSC model achieves high performance on sentiment classification while being exceptionally efficient, making it ideal for production environments. It was trained on the custom-built
Danie1Arias/sentiment-analysis-catalan-reviews dataset, which was specifically created and balanced for this task.
Intended Uses
This model is designed for analyzing the sentiment of user-generated content in Catalan. It's particularly well-suited for:
- Customer Feedback Analysis: Automatically classify customer reviews on products or services.
- Social Media Monitoring: Track public opinion and brand sentiment on social platforms.
- Opinion Mining: Analyze sentiment in forum posts and comments.
Model Details
- Architecture: RoBERTa (encoder-only)
- Base Model:
projecte-aina/roberta-base-ca-v2
- Language: Catalan (
ca)
- Fine-tuning Task: Sequence Classification
- Labels:
negative, neutral, positive
Training Procedure
The model was fine-tuned for 5 epochs using the AdamW optimizer with a learning rate of 2e-5, a batch size of 32, and a weight decay of 0.01. A linear learning rate scheduler with a warmup phase over the first 10% of training steps was employed. The final model selected was the one that achieved the highest Quadratic Weighted Kappa (QWK) score on the validation set, ensuring optimal generalization.
Evaluation
The model was evaluated on a held-out test set, achieving the following results:
| Metric | Score |
|---|
| Accuracy | 83.69% |
| F1 Macro | 0.8186 |
| Quadratic Weighted Kappa (QWK) | 0.8715 |
Limitations and Bias
- This model is specialized for general sentiment in social media and reviews. Its performance may degrade on highly specialized or technical domains (e.g., legal or medical texts).
- Like all language models, it may struggle with sarcasm, irony, or complex figurative language.
- The training data is sourced from public forums; therefore, the model may reflect biases present in that data.
Usage Example
Here’s how to load and use the model to get human-readable predictions:
1from transformers import AutoTokenizer, AutoModelForSequenceClassification
2import torch
3
4# Load the model and tokenizer
5model_name = "Danie1Arias/CSXSC"
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForSequenceClassification.from_pretrained(model_name)
8
9# Input text
10text = "El servei ha estat una mica lent, però el menjar era boníssim."
11# -> "The service was a bit slow, but the food was delicious."
12
13# Tokenize and predict
14inputs = tokenizer(text, return_tensors="pt")
15outputs = model(**inputs)
16predicted_class_id = torch.argmax(outputs.logits, dim=1).item()
17
18# Map the prediction ID to the label name
19predicted_label = model.config.id2label[predicted_class_id]
20
21print(f"Text: '{text}'")
22print(f"Predicted Label: {predicted_label}")
23# Expected output: Predicted Label: positive
Citation & Repository
This model was developed as part of a Master's Thesis. If you use this model or its dataset in your research, please cite the original work.
The full code for data processing, training, and evaluation is available on GitHub:
https://github.com/Danie1Arias/CSXSC/1@mastersthesis{arias2025csxsc,
2 title={From Traditional to Large Language Models: A Novel NLP-Based Model for Sentiment Analysis in Social Media},
3 author={Arias Cámara, Daniel},
4 year={2025},
5 school={Rovira i Virgili University}
6}
