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Add logistic regression sentiment analysis
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README.md
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README.md
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@ -227,32 +227,30 @@ nlp = en_core_web_sm.load()
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doc = nlp("This is a sentence.")
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```
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📖 **For more info and examples, check out the
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[models documentation](https://spacy.io/docs/usage/models).**
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## 📊 Custom Sentiment Analysis with Logistic Regression
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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.
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## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based)
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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.
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### Running the Model
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To run the logistic regression model:
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```bash
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python pure_Logistic.py
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```
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```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results.
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### Testing and Evaluation
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To run tests and evaluate the model's performance:
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To run tests and evaluate the model's performance, use:
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```bash
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python test_pure_logistic.py
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```
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To use the model in your own code:
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```python
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In your test script, import the PureLogisticTextCategorizer class for evaluation:
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```bash
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from pure_Logistic import PureLogisticTextCategorizer
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# Initialize and use the classifier
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categorizer = PureLogisticTextCategorizer()
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```
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This enables you to evaluate the logistic regression classifier on your test cases.
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📖 **For more info and examples, check out the
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[models documentation](https://spacy.io/docs/usage/models).**
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## ⚒ Compile from source
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@ -311,4 +309,4 @@ Alternatively, you can run `pytest` on the tests from within the installed
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```bash
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pip install -r requirements.txt
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python -m pytest --pyargs spacy
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```
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```
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