Text Analysis

Unlock the power of unstructured text data with AI-powered analysis. Extract insights, classify content, and understand sentiment from any text-based data.

What insights can I get from text data?

Text analysis transforms unstructured text into structured, analyzable insights using advanced language models.

What the AI Agent Does:

• Extracts insights from unstructured text using AI
• Classifies content into categories you define
• Scores sentiment between opposing concepts
• Counts and analyzes text length and word usage
• Identifies patterns in large volumes of text

Automatic Classification

• “Classify customer feedback into complaint types”
• “Categorize support tickets by department”
• “Group survey responses by topic”
• “Organize emails by priority level”

Custom Categories

• “Classify reviews as product vs service issues”
• “Categorize social media mentions by intent”
• “Group comments by urgency level”
• “Classify documents by business function”

Complex Categorization

• “What multiple topics does each review cover?”
• “Which documents contain both pricing and features?”
• “Classify feedback that mentions multiple products”
• “Identify content with overlapping themes”

Sentiment Scoring

• “How positive or negative is customer feedback?”
• “Score employee satisfaction in survey responses”
• “Analyze sentiment trends over time”
• “Compare sentiment across different segments”

Custom Sentiment Scales

• “Score satisfaction from very dissatisfied to very satisfied”
• “Rate confidence from uncertain to highly confident”
• “Measure urgency from low to critical”
• “Assess sentiment from frustrated to delighted”

Concept Comparison

• “Score text between satisfied and dissatisfied”
• “Rate content from positive to negative”
• “Score feedback from poor to excellent”
• “Compare text along semantic scales”

Key Information

• “Extract key themes from meeting notes”
• “Identify main topics and concepts”
• “Pull out product-related themes mentioned”
• “Extract conceptual patterns from text”

Structured Data Creation

• “Create clusters of similar content”
• “Extract thematic categories from feedback”
• “Identify and organize conceptual themes”
• “Group similar text content together”

Theme Identification

• “What are the main themes in customer feedback?”
• “Identify common topics in survey responses”
• “Find recurring patterns in complaints”
• “Discover emerging topics in social media”

Topic Trending

• “How have discussion topics changed over time?”
• “Which themes are becoming more common?”
• “What new issues are emerging?”
• “Track topic evolution across periods”

Automatic Grouping

• “Group similar customer comments together”
• “Cluster documents by content similarity”
• “Organize feedback into natural categories”
• “Find documents with similar themes”

Text Characteristics

• “Analyze the length and complexity of responses”
• “How detailed are different types of feedback?”
• “What’s the reading level of customer communications?”
• “Compare response quality across channels”

Language Patterns

• “What language patterns indicate satisfaction?”
• “How do communication styles vary by segment?”
• “What writing patterns predict outcomes?”
• “Identify formal vs informal communication styles”

Quality Scoring

• “Score the helpfulness of customer reviews”
• “Rate the completeness of survey responses”
• “Assess the quality of support interactions”
• “Evaluate content usefulness and relevance”

Support Optimization

• “Classify support tickets by issue complexity”
• “Identify tickets requiring urgent attention”
• “Extract common problems from chat logs”
• “Analyze resolution effectiveness from follow-ups”

Customer Experience

• “What drives positive vs negative service experiences?”
• “Identify pain points in customer journeys”
• “Extract improvement suggestions from feedback”
• “Analyze satisfaction drivers from comments”

Content Performance

• “Which marketing messages resonate most?”
• “Analyze customer responses to campaigns”
• “Extract objections from sales conversations”
• “Identify compelling value propositions”

Lead Quality Assessment

• “Score lead quality from inquiry text”
• “Classify leads by buyer intent”
• “Extract needs and requirements from discussions”
• “Predict conversion likelihood from communications”

Feature Feedback

• “What product features do customers mention most?”
• “Extract improvement suggestions from reviews”
• “Identify usability issues from support tickets”
• “Analyze feature requests by priority”

User Experience Research

• “Extract pain points from user interviews”
• “Classify usability feedback by severity”
• “Identify workflow bottlenecks from user stories”
• “Analyze onboarding feedback for improvements”

Patient Feedback Analysis

• “Classify patient concerns by category”
• “Extract symptoms and side effects mentioned”
• “Analyze treatment satisfaction patterns”
• “Identify care quality improvement areas”

Research Data Processing

• “Extract key findings from research notes”
• “Classify survey responses by research themes”
• “Identify patterns in qualitative interviews”
• “Organize literature review insights”

Core Text Functions

• extract: Extract themes and concepts from text using semantic clustering
• classify: Categorize text into predefined categories
• score: Rate text along semantic scales (e.g., positive to negative)
• word_count: Count words and basic text statistics

Cross-Group Comparisons

• “Compare feedback themes between customer segments”
• “How do text classifications vary by demographics?”
• “Compare semantic scores across product lines”
• “Analyze text patterns across different groups”

Effective Text Questions

• Be specific about what insights you want to extract
• Define categories or sentiment scales clearly
• Specify the type of text analysis needed
• Provide context for better interpretation

Example Progressions:

1. “What themes appear in customer feedback?”
2. “Classify feedback by product area and sentiment”
3. “Which product issues have the most negative sentiment?”
4. “What specific improvements do customers suggest for each issue?”

Text Data Optimization

• Ensure text fields contain sufficient content for analysis
• Clean obvious formatting issues or artifacts
• Consider combining related text fields
• Provide context about text source and purpose