Overview：
This repository contains two Jupyter notebooks that are part of a project focusing on emotional analysis in text. The project uses machine learning techniques to classify text based on the emotions expressed.
Notebooks：
train.ipynb: This notebook is dedicated to training our machine learning model. It includes code for data preprocessing, model selection, training, and evaluation. Key libraries used include scikit-learn, pandas, and numpy.
testEmotion.ipynb: This notebook is used for testing the trained model. It demonstrates how the model can be applied to new datasets to predict emotional content.
Dataset(CASIA):
You can download the dataset in google drive:
https://drive.google.com/file/d/1uRJ1-augLhJw5wvDOL1zjKBLOLSIzXAk/view?usp=sharing
Requirements：
Python 3.9 Jupyter Notebook Libraries: joblib, scikit-learn, pandas, numpy (see notebooks for specific versions and additional dependencies) Installation to use these notebooks:
Clone the repository. Install the required Python libraries (refer to the notebooks for specific commands and dependencies). Usage Use train.ipynb to train the model with your dataset. Use testEmotion.ipynb to apply the trained model to new data for emotion prediction. Contribution Contributions to this project are welcome. Please feel free to fork the repository and submit pull requests.