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A sentiment analyzer for Amazon reviews using machine learning algorithms like Logistic Regression and Support Vector Machines.

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Amazon Reviews Sentiment Analyser

😍😑😭

Overview

This project focuses on sentiment analysis using machine learning and natural language processing techniques. The goal is to develop a Streamlit app capable of analyzing sentiments in various scenarios, including single-line reviews, multiple reviews from CSV files, and product reviews from Amazon URLs.

Project Structure

  • B8_Amazon: Contains Jupyter notebooks with exploratory data analysis and model development.
  • reviewscrapper.py: Includes Python scripts for web scraping reviews for a certain URL.
  • review_analyzer.py: Houses the Streamlit app code for interactive sentiment analysis.
  • models.p: Stores serialized models for sentiment analysis.
  • requirements.txt: Lists the project dependencies for reproducibility.
  • config.toml: Configuration for the Streamlit app theme.

Setup

  1. Clone the repository:

    git clone https://github.com/amri-tah/Amazon-Review-Sentiment-Analysis.git
  2. Navigate to the project directory:

    cd Amazon-Review-Sentiment-Analysis
  3. Install dependencies:

    pip install -r requirements.txt
  4. Open terminal and run the Streamlit app:

    streamlit run review_analyzer.py

Usage

  1. Explore and run Jupyter notebook B8_Amazon.ipynb folder for data analysis and model development.

  2. Execute Python scripts in the reviewscrapper.py for web scraping.

  3. Run the Streamlit app for interactive sentiment analysis:

    streamlit run review_analyzer.py

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A sentiment analyzer for Amazon reviews using machine learning algorithms like Logistic Regression and Support Vector Machines.

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