Sonar
Sonar: AI Social Listening & Sentiment Analysis Tool
Sonar uses machine learning and NLP to automate social listening, filtering out the noise so you only see conversations that truly matter to your brand.
Overview
Sonar is a semantic analysis platform built to take the manual grunt work out of social listening. Instead of relying on clunky boolean search strings, Sonar trains on your specific query using sample data, then automatically scans conversations across Twitter and Reddit to surface only the posts that are genuinely relevant. By understanding context, language patterns, and word relationships rather than just keyword matches, it delivers far cleaner, more accurate results than traditional keyword-based monitoring tools.
Once relevant conversations are identified, Sonar applies NLP-driven sentiment analysis to classify each mention as positive, negative, or neutral, giving teams a real-time pulse on public perception. Its topic modelling feature further organizes this data by theme, making it easy to spot emerging trends or recurring pain points. Sonar's developers claim its purpose-built approach outperforms generic AI models like ChatGPT-3 or Amazon Comprehend when it comes to sentiment accuracy, positioning it as a specialized alternative for teams who need reliable, high-volume conversation analysis.
Whether you're tracking brand health, benchmarking against competitors, or researching market sentiment, Sonar turns an overwhelming stream of social chatter into structured, actionable insight in seconds.
Capabilities & Features
- Social Listening
- Sentiment Analysis
- Topic Modelling
- Semantic Analysis
- Machine Learning
- Natural Language Processing
- NLP
- Social Data Insights
Core Features
- Automated Social Listening
- AI-Powered Sentiment Analysis
- Topic Modelling
- Semantic Analysis Engine
- Natural Language Processing (NLP)
- Query Training on Custom Test Data
Use Cases
- Filtering thousands of social posts down to only what's relevant to a specific query
- Mapping key themes and topics emerging from social conversations
- Comparing brand sentiment against competitors
- Tracking conversation volume and sentiment trends by topic over time
- Replacing manual boolean search workflows with automated, trained queries
Best For
- Social Media Managers
- Marketing Professionals
- Brand Managers
- Market Researchers
- Data Analysts
Pros
- •Automates tedious manual social listening and boolean search work
- •Trained semantic queries reduce irrelevant noise compared to keyword matching
- •Built-in sentiment analysis claimed to outperform generic AI tools
- •Topic modelling helps quickly surface trends without manual tagging
- •Streamlined workflow from data collection to sentiment to topic tagging
Cons
- •Limited to only Twitter and Reddit as data sources
- •Requires upfront training with test data before queries become accurate
- •No publicly listed pricing information available
- •May require some learning curve to train queries effectively for niche use cases
How to Use
1. Select your data sources — currently Twitter and Reddit are supported. 2. Train Sonar on your specific query using sample/test data so it understands what 'relevant' looks like. 3. Let Sonar filter the incoming stream down to only the conversations that match your query. 4. Review the automated sentiment analysis to see how each piece of content is emotionally classified. 5. Browse relevant data organized by topic tags to spot themes and trends.
Frequently Asked Questions
Connect & Contact
Pricing
No public pricing details are currently available for Sonar; prospective users will likely need to contact the team directly for plan and cost information.
Pricing data is provided as a summary. Visit the vendor website for full tier details.