Dear Akhil Kumar ,

The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain.

Article Transfer Learning for Sentiment Analysis Using BERT Based Su...

Regards,

Shafagat

Akhil Kumar There are numerous potential methods for increasing the accuracy of a BERT-CNN model for sentiment analysis:

1. Fine-tune the BERT model for sentiment analysis on a bigger dataset.

2. Increase the quantity of training data accessible by using approaches such as data augmentation.

3. Experiment with various topologies and hyperparameters for the model's CNN component.

4. Use transfer learning techniques to incorporate information from pre-trained models on related tasks.

5. To aggregate the predictions of numerous BERT-CNN models, use ensemble techniques.

6. On your dataset, experiment with different pre-processing approaches like as stemming, lemmatization, and stopword removal.

7. Play around with various optimizers and learning rate plans.

8. Experiment with the model on various datasets or different divisions of the same dataset.

9. Use several assessment measures such as F1-score, accuracy, recall, and so on.

10. You may also experiment with alternative structures such as transformer-XL, RoBERTa, ALBERT, and so on.

These are some ideas for improving the accuracy of your BERT-CNN model for sentiment analysis. Experiment with several ways to find which one works best for your unique purpose and dataset.