Functional requirements of Sentiment Analysis of Social Media Data with non-functional

Functional Requirements

1. Data Collection
   • Social Media Integration: Connect with social media platforms (e.g., Twitter, Facebook, Instagram) via APIs to collect posts, comments, and interactions.
   • Data Aggregation: Aggregate data from multiple sources and handle large volumes of data efficiently.
2. Data Preprocessing
   • Text Cleaning: Remove noise from the data, such as URLs, special characters, and irrelevant content.
   • Tokenization: Break down text into individual tokens (words or phrases) for further analysis.
   • Normalization: Normalize text by converting to lower case, removing stop words, and stemming or lemmatizing words.
3. Sentiment Analysis
   • Sentiment Classification: Classify text into sentiment categories such as positive, negative, or neutral.
   • Emotion Detection: Identify specific emotions (e.g., joy, anger, sadness) expressed in the text if required.
   • Aspect-Based Sentiment Analysis: Analyze sentiment concerning specific aspects or topics (e.g., product features, service quality).
4. Real-Time Analysis
   • Streaming Data Processing
     Advertisement
     : Process and analyze social media data in real-time or near real-time to provide up-to-date sentiment insights.
   • Alerting: Generate alerts for significant sentiment changes or trending topics.
5. Visualization and Reporting
   • Dashboards: Provide interactive dashboards to visualize sentiment trends, distribution, and key metrics.
   • Reports: Generate and export reports on sentiment analysis results, including summaries and detailed insights.
6. User Interaction
   • Search and Filter: Allow users to search and filter data based on criteria such as keywords, hashtags, date ranges, and sentiment.
   • Data Export: Enable users to export data and analysis results in various formats (e.g., CSV, PDF).
7. Integration with Other Systems
   • APIs: Provide APIs for integration with other applications or systems for automated data exchange and analysis.
   • CRM Integration: Integrate with Customer Relationship Management (CRM) systems to enhance customer insights and engagement.
8. Data Storage and Management
   • Database Management: Store and manage collected data, processed results, and metadata in a structured database.
   • Data Archiving: Archive historical data for long-term storage and future analysis.
9. Customization and Configuration
   • Sentiment Models: Allow customization of sentiment analysis models or use pre-trained models suitable for specific industries or topics.
   • User Preferences: Enable users to configure analysis settings, including sentiment thresholds and language options.

Non-Functional Requirements

1. Performance
   • Scalability: Design the system to handle varying volumes of social media data, including high-throughput and peak loads.
   • Response Time: Ensure low-latency processing and analysis to provide real-time or near real-time insights.
2. Reliability
   • System Uptime: Maintain high system availability with minimal downtime to ensure continuous data collection and analysis.
   • Fault Tolerance: Implement fault-tolerant mechanisms to ensure system operation in case of hardware or software failures.
3. Usability
   • User Interface: Design an intuitive and user-friendly interface for interacting with sentiment analysis results and dashboards.
   • Training and Support: Provide training materials and support resources to help users effectively use the system.
4. Security
   • Data Protection: Implement strong security measures to protect social media data and analysis results from unauthorized access and breaches.
   • User Authentication: Use secure authentication methods to manage user access and permissions.
5. Maintainability
   • Code Quality: Maintain a well-documented and modular codebase to facilitate ongoing maintenance and updates.
   • Update Management: Provide a structured process for deploying updates and patches, including testing and user notifications.
6. Compatibility
   • Social Media APIs: Ensure compatibility with various social media APIs and data formats.
   • Browser and Device Support: Ensure compatibility with major web browsers and devices used by end-users.
7. Compliance
   • Data Privacy: Adhere to data privacy regulations and standards, including GDPR, CCPA, or other relevant laws.
   • Ethical Use: Ensure ethical use of social media data and respect user privacy and consent.
8. Backup and Recovery
   • Data Backup: Implement regular backups of social media data, analysis results, and system configurations to prevent data loss.
   • Disaster Recovery: Develop and maintain a disaster recovery plan to restore system functionality and data in case of major failures or emergencies.