Using Semantic Search

What is Semantic Search?

Semantic search understands the meaning and context behind your queries, not just exact keyword matches. It uses AI to find relevant feedback based on concepts, ideas, and relationships, making it much more powerful than traditional keyword search.

How Semantic Search Works

🧠 AI-Powered Understanding

Semantic search uses advanced AI models to understand the meaning behind your search terms and match them with relevant feedback, even when the exact words don't match.

Example:

Searching for "user interface problems" will find feedback about "UI issues", "interface bugs", "design problems", and "usability concerns" - even though these use different words.

Key Benefits

Better Results

• Finds relevant content even with different wording
• Understands synonyms and related terms
• Matches concepts, not just keywords
• Reduces false positives

Natural Queries

• Use natural language questions
• No need to guess exact keywords
• Works with conversational queries
• Understands context and intent

Effective Semantic Search Techniques

Natural Language Queries

✅ Good Examples

• "What are users saying about the mobile app performance?"
• "Find feedback about login issues"
• "Show me complaints about slow loading times"
• "What do users think about the new dashboard design?"

❌ Less Effective

• "mobile app performance" (too generic)
• "login" (too broad)
• "slow" (lacks context)
• "dashboard" (not specific enough)

Question-Based Searches

Frame your searches as questions to get better semantic results:

Problem-Focused

• "What problems are users experiencing?"
• "Why are users frustrated with checkout?"
• "What's causing the most complaints?"
• "Where are users getting stuck?"

Feature-Focused

• "What do users want to see improved?"
• "How are users responding to new features?"
• "What features are most requested?"
• "What do users love about the product?"

Advanced Semantic Search Strategies

Context-Aware Searches

Include Context

• "Mobile app crashes on iOS devices"
• "Checkout process issues for new users"
• "Performance problems during peak hours"
• "User interface confusion for first-time users"

Use Descriptive Terms

• "Frustrating user experience" instead of "bad UX"
• "Critical security concerns" instead of "security"
• "Confusing navigation flow" instead of "navigation"
• "Slow response times" instead of "slow"

Sentiment-Based Searches

Positive Sentiment

• "What features do users love?"
• "Positive feedback about recent updates"
• "User appreciation and compliments"
• "Successful user experiences"

Negative Sentiment

• "User complaints and frustrations"
• "Critical issues and problems"
• "Negative feedback about changes"
• "User dissatisfaction and concerns"

Combining Semantic and Keyword Search

Hybrid Search Strategies

The most effective searches combine semantic understanding with specific keywords:

Example 1: Feature + Problem

"Mobile app login problems" - combines semantic understanding of "problems" with specific "mobile app" and "login" terms

Example 2: Context + Action

"Users can't complete checkout process" - semantic understanding of the problem with specific "checkout" context

Semantic Search Best Practices

✅ Do

• Use complete sentences and questions
• Include relevant context and details
• Try different ways to express the same concept
• Combine semantic search with filters
• Use descriptive, specific language
• Experiment with different query structures

❌ Don't

• Use overly generic single words
• Assume the AI knows your specific context
• Use technical jargon without explanation
• Give up after one search attempt
• Ignore the results and try different approaches
• Mix too many concepts in one search

Troubleshooting Semantic Search

Common Issues

• Too many results: Add more specific context to your query
• Too few results: Try broader terms or different phrasing
• Irrelevant results: Be more specific about what you're looking for
• Missing results: Try synonyms or related terms

Improving Search Results

• Refine your query: Add more context or be more specific
• Use filters: Combine with date, bucket, or category filters
• Try variations: Experiment with different ways to phrase your search
• Check spelling: Ensure your query is spelled correctly
• Use examples: Look at similar successful searches

Advanced Semantic Search Features

AI-Powered Suggestions

• Query Suggestions: AI suggests related search terms
• Auto-complete: Intelligent query completion
• Related Searches: Suggestions based on your query
• Trending Topics: Popular search terms in your data

Smart Result Ranking

• Relevance Scoring: Results ranked by semantic similarity
• Context Awareness: Considers your search history
• Recency Weighting: Newer results may rank higher
• User Behavior: Learns from your interaction patterns

💡 Pro Tip

Start with broad semantic searches to understand the scope of your data, then narrow down with more specific queries. The AI gets better at understanding your needs the more you use it, so don't be afraid to experiment with different search approaches.