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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.