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SentimentScope: Intelligent Aspect-based Sentiment Analysis for Product Reviews

This repository contains an implementation of an Aspect-based Sentiment Analysis (ABSA) algorithm to identify product review categories and corresponding sentiments for each category. The project focuses on analyzing laptop reviews provided in XML format.

Table of Contents

• Introduction
• Features
• Technologies Used
• Requirements
• Installation
• Usage
• Dataset
• Project Structure
• Acknowledgements
• License

Introduction

SentimentScope is an advanced tool for performing Aspect-based Sentiment Analysis on product reviews. It parses reviews from XML files, identifies various categories (aspects) within the reviews, and determines the sentiment (positive, negative, or neutral) associated with each category. This project specifically targets laptop reviews.

Features

• Aspect Identification: Extracts different aspects (e.g., battery life, performance, design) from laptop reviews.
• Sentiment Analysis: Determines the sentiment for each identified aspect.
• XML Parsing: Reads and processes reviews from XML files.
• Customizable: Can be adapted to other product review datasets and different file formats.

Technologies Used

• Python: Main programming language.
• NLTK: For natural language processing tasks.
• TextBlob: For sentiment analysis.
• xml.etree.ElementTree: For parsing XML files.

Requirements

• Python 3.6+
• NLTK
• TextBlob
• xml.etree.ElementTree

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sentimentscope.git
   cd sentimentscope

2. Install the required libraries:

   pip install nltk textblob

3. Download NLTK data:

   import nltk
   nltk.download('punkt')

Usage

1. Prepare your dataset: Ensure you have an XML file containing laptop reviews.

2. Run the analysis:

3. View the results: The output will provide a breakdown of sentiments (positive, negative, neutral) for each identified aspect.

Dataset

The dataset used for this project consists of laptop reviews in XML format. Each review contains detailed comments about various aspects of the laptop, such as battery life, performance, design, screen, and price.

Project Structure

sentimentscope/
├── absa.py                 # Main script for ABSA

├── README.md               # Project README file
└── dataset/                # Directory containing the XML dataset

Acknowledgements

• NLTK for providing natural language processing tools.
• TextBlob for simplifying sentiment analysis.
• xml.etree.ElementTree for XML parsing.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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