Analyze the sentiment of your web data automatically with AgentQL
Swiftask partners with AgentQL to extract and analyze the emotions behind reviews, comments, and web mentions. Get actionable customer insights, instantly.
Result:
Turn web noise into strategic decisions with precise, automated sentiment analysis.
AI Agents
agentql
Connector agentql · Secure OAuth 2.0
Customer sentiment is lost in the volume of web data
Monitoring public opinion on your products is critical, but the volume of data is manually unmanageable. Traditional tools are rigid, expensive, and struggle to adapt to changing website structures.
Main negative impacts:
Unstructured and unreadable data
Customer reviews are scattered across dozens of platforms. Without automated extraction, this valuable data remains untapped.
Fragility of traditional scrapers
As soon as a website changes its structure, your extraction tools break. You lose days fixing your pipelines.
Insufficient reactivity
Customer sentiment changes fast. If your analysis takes days, you are reacting to issues that are no longer relevant.
With Swiftask and AgentQL, you automate web data extraction based on natural language. Your Swiftask AI agents then analyze the sentiment of this data in real-time, without constant technical maintenance.
BEFORE / AFTER
What changes with Swiftask
Without Swiftask + AgentQL
A marketing team spends hours copying and pasting customer reviews into a spreadsheet. They use scraping tools that break regularly, requiring technical intervention. The analysis is done once a month, too late to adjust the strategy.
With Swiftask + AgentQL
Your AI agents automatically query target sites via AgentQL. Data is extracted and immediately analyzed by Swiftask. You receive a daily summary of sentiment trends directly in your workflow.
4 steps to automate your sentiment analysis
1
STEP 1 : Define your web sources in AgentQL
Identify the websites to monitor (reviews, social networks, forums). AgentQL allows robust extraction thanks to natural language.
2
STEP 2 : Connect AgentQL to your Swiftask agent
Configure the Swiftask agent to call the data extracted by AgentQL as an input source.
3
STEP 3 : Configure sentiment analysis
Give your Swiftask agent the mission to classify the extracted data: positive, negative, or neutral, with contextual explanation.
4
STEP 4 : Automate alerts
Set thresholds: receive an immediate notification if a negative trend is detected on your products.
Intelligent sentiment analysis capabilities
The agent analyzes not only the polarity (positive/negative), but also the intention, urgency, and specific subjects mentioned in the comments.
- Target connector: The agent performs the right actions in agentql based on event context.
- Automated actions: Resilient web data extraction. Automatic review classification. Summary of customer pain points. Real-time alerts on negative sentiment spikes.
- Native governance: The combination of AgentQL + Swiftask ensures your data pipelines remain operational even if source websites evolve.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-agentql@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Why choose this duo for your monitoring
Reduced maintenance
AgentQL adapts to website changes. No more updating CSS selectors manually.
Real-time insights
Don't depend on monthly reports. Analyze sentiment as soon as a new review is published.
Contextual precision
Swiftask's AI understands nuances, irony, and context specific to your industry.
Seamless integration
Inject analysis results directly into your CRM or project management tools.
Data governance
Centralize all your sentiment data and ensure compliance for your extraction processes.
Security and compliance
Swiftask applies enterprise-grade security standards for your agentql automations.
- Ethical extraction: Respect for scraping rules and robots.txt policies of target sites.
- Secure processing: Your data is processed in isolated and secure environments by Swiftask.
- Confidentiality: Your extraction queries and analysis models are private and protected.
- Full audit trail: Keep track of all extracted data and generated analyses for your compliance reports.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Gain operational efficiency
| Metric | Before | After |
|---|---|---|
| Scraping maintenance time | Several hours/week | Near zero |
| Analysis delay | Several days | Minutes |
| Source coverage | Limited by technical complexity | Unlimited |
| Data reliability | Low (changing sites) | High (AgentQL resilience) |
Take action with agentql
Turn web noise into strategic decisions with precise, automated sentiment analysis.