Example Concept: To improve sentiment analysis accuracy, start by ensuring high-quality data preprocessing, such as removing noise and normalizing text. Experiment with different models, like LSTM or BERT, which are well-suited for capturing context in text. Fine-tuning hyperparameters and using techniques like cross-validation can further optimize model performance. Additionally, augmenting your dataset with more labeled examples or using transfer learning can help the model generalize better.