In this liveProject, you’ll augment text-based training data for a sentiment analysis algorithm with artificially generated positive reviews. You’ll merge the synthetic positive reviews with an unbalanced dataset focused on negative reviews, thereby creating a balanced dataset for your model to train on. You’ll train your model, then evaluate its metrics using sklearn.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for Python programmers looking to augment training data. To begin this liveProject, you will need to be familiar with:
- Intermediate Python, with basics of NumPy and pandas
- Basics of Jupyter Notebook
- Basics of Google Colab notebooks
- Basics of TensorFlow
- Basics of NLP
- Basics of deep learning
you will learn
In this liveProject, you’ll learn how to augment unbalanced datasets with synthetic training data for sentiment analysis.
- Merging training and synthetic data
- Building and training a text classification model
- Scoring text data with the model
- Evaluating model performance with fundamental classification metrics