Add logistic regression sentiment analysis

README_Sentiment_Analysis_spaCy.md

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+Sentiment Analysis Using Logistic Regression (using spaCy)
+This repository provides a Text Categorization model using logistic regression built on spaCy without using scikit-learn. It aims to classify text as positive or negative based on custom logistic regression implementation. The project includes training and testing scripts for sentiment analysis.
+
+💬 Project Highlights
+Custom Logistic Regression Model: Implemented from scratch using Python.
+Natural Language Processing: Leveraging spaCy for text preprocessing (tokenization, vectorization).
+No External ML Libraries: The project does not rely on external libraries like scikit-learn.
+
+✨ Features
+Text Classification: Sentiment analysis using logistic regression.
+Preprocessing: spaCy is used to tokenize and vectorize text data.
+Evaluation Tools: Includes scripts to evaluate the performance of the model on test datasets.
+Modular Design: Easily replace datasets and tweak preprocessing steps.
+
+📦 Installation
+To begin with, you'll need Python 3.7 or higher and install spaCy and its required language model. Here's how to set it up:
+pip install spacy
+python -m spacy download en_core_web_lg
+
+🚀 Quickstart
+Clone the repository:
+git clone https://github.com/yourusername/sentiment-analysis-logisticregression.git
+cd sentiment-analysis-logisticregression
+
+Run the logistic regression model:
+python pure_Logistic.py
+Test the model:
+python test_pure_logistic.py
+
+🗂️ Project Structure
+│
+├── spacy/                   # Contains spaCy-related pipeline and models
+│   ├── pipeline/textcat/pure_Logistic.py     # SpaCy text classification models
+│   └── pipeline/test_text/test_pure_Logistic.py     # Logistic regression implementation
+└── README_Sentiment_Anlysis_spaCy.md                # This file
+
+🔧 Usage
+For using the model, you don't need to re-implement any functionality. The PureLogisticTextCategorizer class, which is defined in pure_Logistic.py, can be directly imported and used in your test scripts.
+
+To execute the model:
+python test_pure_logistic.py
+In this file, you can import the logistic regression class as:
+from pure_Logistic import PureLogisticTextCategorizer
+This will allow you to run predefined test cases and evaluate the performance of the logistic regression model on your test data.
+
+
+📊 Model Details
+The model performs sentiment analysis by leveraging spaCy's powerful text preprocessing capabilities. The logistic regression classifier is implemented manually, without any help of scikit-learn or any other major machine learning libraries.
+
+spaCy is used to preprocess text, including tokenization, vectorization, and feature extraction.
+Logistic Regression is implemented in pure Python for binary classification (positive vs negative sentiment).
+
+🛠️ Development
+Requirements
+Python 3.7+
+spaCy
+
+To install the required packages, run:
+pip install spacy
+python -m spacy download en_core_web_lg
+Contributing
+Feel free to fork the repository, make updates, and submit pull requests. Suggestions for improvements are always welcome.