AI Data Enrichment and Labeling: Text Sentiment Analysis
This tutorial demonstrates how to use Corvic AI to enrich your text data with AI-powered sentiment analysis labels. Learn how to automatically analyze sentiment in your text data and add structured labels that enhance your embeddings and enable more sophisticated analysis.What You’ll Learn
- Enriching text data with AI-powered sentiment analysis
- Automatically labeling text with sentiment scores
- Using sentiment labels to enhance embeddings
- Building applications that leverage sentiment analysis
Key Concepts
AI-Powered Sentiment Analysis
Corvic AI enables you to automatically analyze sentiment in your text data and add structured labels such as positive, negative, or neutral sentiment scores. This enrichment enhances your data with valuable metadata that can improve embeddings and enable sentiment-based queries and analysis.Data Enrichment Pipeline
The sentiment analysis enrichment process analyzes your text data, extracts sentiment information, and adds labels that become part of your enriched dataset. These labels can then be used in Corvic Tables, embeddings, and agents to enable sentiment-aware applications.Enriching Data with Sentiment Analysis
Step 1: Prepare Your Text Data
Ensure your text data is properly formatted and accessible. This can include customer reviews, social media posts, support tickets, survey responses, or any other text-based data where sentiment analysis would be valuable.Step 2: Configure Sentiment Analysis
Configure the sentiment analysis enrichment by specifying which text fields to analyze and how sentiment labels should be structured. Corvic AI automatically processes your text and generates sentiment labels.Step 3: Review and Use Enriched Data
Review the generated sentiment labels and use them in your Corvic Tables and embeddings. The sentiment information enhances your embeddings and enables sentiment-based search, filtering, and analysis in your applications.Benefits
Sentiment analysis enrichment adds valuable metadata to your text data, enabling sentiment-aware search, filtering, and analysis. This enhances your embeddings with sentiment information and allows you to build applications that understand and respond to sentiment in your data.Best Practices
Use sentiment analysis enrichment for text data where sentiment is meaningful, such as customer feedback, reviews, or social media content. Review sentiment labels for accuracy and adjust thresholds as needed. Combine sentiment labels with other enrichments for comprehensive data analysis.Related Documentation
Data Enrichment
Learn about code-based augmentation for data enrichment.
Corvic Tables
Use enriched data in your Corvic Tables for distributed processing.
Spaces
Generate embeddings from sentiment-enriched data.
Agents
Build sentiment-aware agents using enriched data.