RoshanGhadge20 / Sentimental_Analysis_MCA

This project implements aspect-based sentiment analysis, a sophisticated natural language processing technique that not only evaluates the sentiment of a text document as a whole but also identifies and analyzes the sentiment associated with specific aspects or features mentioned within the text.

Aspect-Based Sentiment Analysis 🎯

Overview 📖

Aspect-Based Sentiment Analysis (ABSA) is a subfield of sentiment analysis that focuses on identifying and analyzing sentiment expressed towards specific aspects or features mentioned in text data. Unlike traditional sentiment analysis, ABSA provides a more granular understanding of sentiment associated with different aspects of products or services, enabling businesses to gain deeper insights into customer opinions, preferences, and feedback.

Objective 🎯

The primary objective of this project is to develop a system capable of analyzing text data and extracting sentiment polarity (positive, negative, or neutral) associated with specific aspects or features mentioned within the text. The key objectives include:

• Aspect Extraction: Implement algorithms to identify and extract specific aspects mentioned in the text using natural language processing (NLP) techniques.
• Sentiment Analysis: Develop models to determine the polarity of sentiments expressed towards each extracted aspect.
• Scalability and Efficiency: Ensure the system can process large volumes of text data in real-time or near real-time.
• Accuracy and Reliability: Strive for high accuracy and reliability in sentiment analysis results through rigorous testing and validation.
• Integration and Deployment: Develop the system for easy integration into existing software applications or workflows.
• Visualization and Reporting: Implement features for visualizing sentiment analysis results and generating insightful reports.
• Feedback Mechanism: Incorporate user feedback to continuously improve the system's accuracy and performance.
• Ethical Considerations: Address ethical issues related to privacy, bias, and fairness in sentiment analysis.

Features ✨

• Granular Insights: Understand not only overall sentiment but also the sentiments associated with specific aspects of products or services.
• Targeted Actions: Enable businesses to take targeted actions to address customer concerns and improve overall satisfaction.
• Domain Adaptability: Applicable across various domains and industries such as e-commerce, hospitality, healthcare, and more.
• Scalability: Capable of analyzing large volumes of text data efficiently.

Installation 🛠️

To install and run the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/RoshanGhadge20/Sentimental_Analysis_MCA.git
   cd Sentimental_Analysis_MCA

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python app.py

Usage 🚀

1. Data Preparation: Ensure your text data is in a suitable format for analysis.
2. Aspect Extraction: Run the aspect extraction module to identify aspects within the text.
3. Sentiment Analysis: Apply the sentiment analysis model to determine sentiment polarity for each aspect.
4. Visualization: Use the visualization tools to generate reports and insights from the analyzed data.

Project Structure 🗂️

• data/: Contains datasets used for training and testing.
• models/: Includes trained models for aspect extraction and sentiment analysis.
• notebooks/: Jupyter notebooks for experimentation and development.
• src/: Source code for the application.
• reports/: Generated reports and visualizations.
• requirements.txt: List of dependencies.