Hungarian Sentence-level Sentiment Analysis Model with XLM-RoBERTa

August 23, 2024

In today’s world, understanding sentiments through textual analysis has become a fundamental part of data science. This blog post will guide you through implementing a Hungarian sentence-level sentiment analysis model using XLM-RoBERTa.

Getting Started

This model has been pretrained and fine-tuned specifically for Hungarian sentiments based on the Hungarian Twitter Sentiment (HTS) Corpus. The model classifies sentiments into five distinct labels:

0: Very Negative
1: Negative
2: Neutral
3: Positive
4: Very Positive

Model Specifications

The backbone of our sentiment analysis rests upon the XLM-RoBERTa base model which has proven to be remarkably effective. The maximum sequence length for input text is set to 128 characters, allowing for effective processing of extensive textual data while fitting within the token limits of the model.

Understanding the Results

Let’s dive into how our model stacks up against others in the domain. Below is a comparison of accuracy metrics:

 Model         HTS2   HTS5  
-------------  ------------- -------------
huBERT         85.56  68.99   
XLM-RoBERTa    85.56  66.50

In this context, you can think of the model’s performance as two runners competing in a race where the finish line is high accuracy. In this race, both XLM-RoBERTa and huBERT cross the finish line neck and neck, showcasing their high accuracy on the HTS2 dataset while revealing interesting discrepancies in HTS5.

Troubleshooting

While using this model, you might encounter some issues. Here are a few common problems and their solutions:

Issue: Low accuracy of predictions.
Solution: Ensure that your input data is clean and relevant to the sentiment classification task. Maybe even retrain with new data to improve results.
Issue: Errors due to exceeding maximum sequence length.
Solution: Truncate or split your input sentences to not exceed 128 characters.
Issue: Slow processing times.
Solution: Ensure you’re utilizing a machine with adequate resources, or consider optimizing your code for performance.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.