Aspect-based sentiment Analysis (ABSA) is a subfield that focuses on identifying and analyzing sentiment expressed toward 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.
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.
- 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.
To install and run the project, follow these steps:
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Clone the repository:
git clone https://github.com/RoshanGhadge20/Sentimental_Analysis_MCA.git cd Sentimental_Analysis_MCA -
Run the application:
python app.py
- Data Preparation: Ensure your text data is in a suitable format for analysis.
- Aspect Extraction: Run the aspect extraction module to identify aspects within the text.
- Sentiment Analysis: Apply the sentiment analysis model to determine sentiment polarity for each aspect.
- Visualization: Use the visualization tools to generate reports and insights from the analyzed data.
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.