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Add logistic regression sentiment analysis

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@ -227,32 +227,30 @@ nlp = en_core_web_sm.load()
doc = nlp("This is a sentence.") doc = nlp("This is a sentence.")
``` ```
📖 **For more info and examples, check out the ## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based)
[models documentation](https://spacy.io/docs/usage/models).** This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews.
## 📊 Custom Sentiment Analysis with Logistic Regression
This implementation includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on datasets like IMDb reviews.
### Running the Model ### Running the Model
To run the logistic regression model: To run the logistic regression model:
```bash ```bash
python pure_Logistic.py python pure_Logistic.py
``` ```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results.
### Testing and Evaluation ### Testing and Evaluation
To run tests and evaluate the model's performance: To run tests and evaluate the model's performance, use:
```bash ```bash
python test_pure_logistic.py python test_pure_logistic.py
``` ```
To use the model in your own code: In your test script, import the PureLogisticTextCategorizer class for evaluation:
```python ```bash
from pure_Logistic import PureLogisticTextCategorizer from pure_Logistic import PureLogisticTextCategorizer
# Initialize and use the classifier
categorizer = PureLogisticTextCategorizer()
``` ```
This enables you to evaluate the logistic regression classifier on your test cases.
📖 **For more info and examples, check out the
[models documentation](https://spacy.io/docs/usage/models).**
## ⚒ Compile from source ## ⚒ Compile from source