| Week |
Tasks |
Completion
|
| Week 4
|
- Read topic-related Acadamic papers to figure basic paradigms
- Brainstorm and present initial ideas for the project
|
✓
|
| Week 5
|
- Learn the standard process for NLP preprocessing.
- Find suitable news datasets and economic crisis labels.
|
✓
|
| Week 6
|
- Initially define the entire project's workflow.
- Configure the development environment and master the relevant software and libraries.
- Finishing data preprocessing on the news dataset.
|
✓
|
| Week 7
|
- Completing the feature engineering construction and basic pipeline for the TF-IDF based model. (Completed)
- Completing the feature engineering construction and basic pipeline for the sentiment dictionary-based model.
|
✓
|
| Weeks 8–9
|
- Choose and train appropriate machine learning models to build feature-to-label mappings.
- Learn and implement cross-validation of timing models to validate model performance.
- Analyze the experimental results and summarize preliminary conclusions.
|
✓
|
| Week 10
|
- Prepare slides for the midterm presentation.(Completed)
- Fill in information on wiki.
|
✓
|
| Week 11
|
- Expand the fine-grained news dataset and replenish the economic analysis metrics.
- Introduce pre-trained models with transformer architecture to optimize the extraction of sentiment features.
- Explore a variety of deep learning and machine learning techniques for optimization.
|
|
| Week 12
|
- Complete project workflows on new datasets with new time series models.
- Compare the results and analyze the correlations between sentiment scores and different financial indicators.
- Decision fusion for enhancing model performance.
|
|
| Week 13
|
- Achieve visual representation to display news trends, sentiment analysis outcomes, and predictive metrics.
- Finalize modifications and refinements for the project's concluding model iterations.
|
|
| Week 14
|
- Write the report.
- Prepare for the final presentation.
|
|