AI-Based Sentiment Analysis Tool: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop an AI-powered tool to analyze and categorize text data into positive, negative, or neutral sentiments.
Scope of the system: Useful for businesses in customer feedback analysis, social media monitoring, and market research.
2. Requirements Analysis
Functional Requirements:
· - Ingest and process text data from multiple sources (e.g., social media, customer reviews).
· - Perform sentiment analysis using machine learning models.
· - Visualize results with charts and graphs.
· - Allow users to filter and search results based on specific criteria.
Non-Functional Requirements:
· - High accuracy in sentiment detection.
· - Scalability to process large datasets.
· - Secure handling of user data.
3. System Design
Architecture:
· - Client-server architecture with a machine learning model at the backend.
· - Frontend for user interaction and data visualization.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of data ingestion, processing, and results visualization.
· - Level 1: Detailed processes for text preprocessing, model prediction, and results generation.
Database Design:
· - Tables: Raw Data, Processed Data, Sentiment Results, Users.
4. Technology Stack
Frontend:
· - Web application frameworks: React, Angular, or Vue.js.
· - Visualization libraries: D3.js, Chart.js, or Plotly.
Backend:
· - Python (Flask or Django) or Node.js for API development.
· - Sentiment analysis libraries like NLTK, TextBlob, or Hugging Face Transformers.
Database:
· - SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Elasticsearch).
Cloud Services:
· - AWS, Google Cloud, or Azure for hosting and model training.
Machine Learning:
· - Algorithms: Naive Bayes, LSTM, or BERT-based models.
5. Implementation
Data Collection:
· - Integrate APIs for data sources like Twitter, Facebook, or web scraping tools.
· - Store raw data securely in the database.
Preprocessing:
· - Clean text data (remove stop words, special characters, etc.).
· - Tokenize and vectorize text for model input.
Model Training and Deployment:
· - Train or fine-tune a sentiment analysis model on labeled datasets.
· - Deploy the model as a RESTful API or using frameworks like TensorFlow Serving or FastAPI.
Visualization and Reporting:
· - Develop dashboards to display sentiment distribution and trends.
· - Allow export of analysis reports in formats like PDF or Excel.
6. Security
Encrypt data during storage and transmission to ensure privacy.
Implement role-based access control for users.
Comply with data protection regulations like GDPR or CCPA.
7. Testing
Unit Testing: Validate text preprocessing, model inference, and API endpoints.
Integration Testing: Ensure seamless communication between frontend, backend, and ML models.
System Testing: Test the tool for accuracy and usability on various datasets.
Performance Testing: Evaluate response times and scalability under heavy workloads.
8. Deployment
Deploy the system on cloud or on-premises infrastructure based on project needs.
Provide user manuals and documentation for system administrators and end users.
Set up monitoring tools to track system performance and user activity.
9. Maintenance and Updates
Regularly update the model with new training data for improved accuracy.
Address user feedback to enhance system functionality and user experience.
Monitor system logs to identify and resolve any issues promptly.
10. Appendix
Glossary of terms.
References and additional resources.