NLP Project: Analyzing Sentiments in Amazon Reviews

Project Overview

This project aims to analyze sentiment from Amazon reviews using natural language processing (NLP) techniques. By leveraging machine learning algorithms, we classify reviews as positive, negative, or neutral, providing insights into customer satisfaction and product feedback.

Features

• Data Preprocessing: Cleaning and preparing the text data for analysis.
• Sentiment Classification: Using machine learning models to classify reviews.
• Visualization: Graphical representation of the sentiment analysis results.
• Model Evaluation: Assessing the performance of the models using various metrics.

Dataset

The dataset used for this project contains Amazon reviews, obtained from Kaggle. Each review includes:

• Review text
• Rating

Installation

To get started with this project, clone the repository and install the necessary dependencies:

git clone https://github.com/your-username/sentiment-analysis-amazon-reviews.git
cd sentiment-analysis-amazon-reviews
pip install -r requirements.txt

Usage

1. Load the Dataset: Import the dataset into your notebook or script.
2. Preprocess the Data: Clean the text data by removing stop words, punctuation, and other irrelevant elements.
3. Train the Model: Use machine learning models to train on the preprocessed data.
4. Evaluate the Model: Assess the performance of the model using metrics like accuracy, precision, recall, and F1-score.
5. Visualize the Results: Create visualizations to represent the sentiment distribution and model performance.

Results

The sentiment analysis results show the distribution of positive, negative, and neutral reviews. Detailed performance metrics of each model are provided, along with confusion matrices and classification reports.