Teradata Sentiment Extraction

Core Capabilities

• Automated table structure analysis via DBC.ColumnsV
• Dynamic SQL generation for TD_SentimentExtractor
• Complete workflow from data preparation to results interpretation
• Data quality validation and preprocessing guidance
• Parameter optimization and tuning

Key Parameters

• TextColumn: Column containing text to analyze
• AnalysisType: 'DOCUMENT' (whole-document) or 'SENTENCE' (per-sentence)
• Accumulate: Columns to pass through to output

Use Cases

1. Customer feedback sentiment analysis
2. Social media monitoring
3. Product review classification
4. Brand perception tracking
5. Support ticket prioritization

Example Usage

-- TD_SentimentExtractor execution
SELECT * FROM TD_SentimentExtractor (
    ON {USER_DATABASE}.{TEXT_TABLE} AS InputTable
    USING
    TextColumn ('{TEXT_COLUMN}')
    AnalysisType ('DOCUMENT')          -- 'DOCUMENT' or 'SENTENCE'
    Accumulate ('{DOC_ID}', '{OTHER_COLUMNS}')
) AS dt;

Scripts Included

Core Analytics Scripts

• table_analysis.sql: Automatic table structure discovery
• preprocessing.sql: Data preparation and feature engineering
• model_training.sql: TD_SentimentExtractor execution
• evaluation.sql: Results analysis and metrics
• complete_workflow_template.sql: End-to-end workflow

Utility Scripts

• data_quality_checks.sql: Comprehensive data validation
• parameter_tuning.sql: Parameter optimization
• diagnostic_queries.sql: Results diagnostics and interpretation

Best Practices

• Always run table_analysis.sql first to understand your data structure
• Validate data quality before executing the analytical function
• Use parameter_tuning.sql to find optimal configuration
• Review diagnostic_queries.sql output for model/results validation

Limitations

• Requires Teradata Vantage 17.20+ with ClearScape Analytics
• Input data must meet function-specific requirements
• Results depend on data quality and parameter configuration

Teradata Sentiment Extraction - ClearScape Analytics skill for Teradata Vantage