AI Text Classifier - Intelligent Content Categorization

Classify any text using AI with custom labels. Perfect for sentiment analysis, content moderation, customer support automation, and more.

Features •

Use Cases •

Label Presets •

Input •

Output •

Cost of Usage

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This actor automatically classifies any text into predefined categories using advanced AI technology. Simply paste your texts, choose a classification preset or define custom labels, and get instant, accurate categorization results. Perfect for automating content analysis, customer support routing, sentiment tracking, and data organization at scale.

✨ Key Features

• 🤖 AI-Powered Classification: Advanced AI technology ensures accurate and consistent text categorization.

• 🚀 High Performance: Processes multiple texts in parallel for fast bulk classification.

• 🏷️ 6 Ready-to-Use Presets: Choose from sentiment analysis, mobile app reviews, customer support, e-commerce reviews, content moderation, and social media categorization.

• ✏️ Custom Labels: Define any classification categories you need for your specific use case.

• 📊 Detailed Results: Get classification results with status tracking and error handling.

• 💼 Enterprise Ready: Handles large volumes of text with automatic error recovery and retry logic.

• 🎯 Flexible Input: Paste texts directly in bulk format - one per line.

• 📈 Real-time Processing: Results are pushed to dataset as they're processed for immediate access.

🎯 Use Cases

Use Case	Description
Sentiment Analysis	Analyze customer reviews, social media posts, survey responses, and feedback to understand customer sentiment.
Content Moderation	Automatically categorize user-generated content to identify spam, harassment, inappropriate content, and flag items for review.
Customer Support Automation	Auto-categorize support tickets by type (question, complaint, technical issue, billing) to route them to the right team.
E-commerce Reviews	Classify product reviews by topic (quality, shipping, price, customer service) to identify trends and improvement areas.
Mobile App Reviews	Categorize app store reviews by UX, UI, bugs, feature requests, and performance issues for product development insights.
Social Media Monitoring	Classify social media posts by engagement type, topic, and sentiment for brand monitoring and marketing analysis.
Lead Scoring	Classify sales inquiries by intent and priority to prioritize high-value leads.
Market Research	Analyze open-ended survey responses and categorize feedback for actionable insights.

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🏷️ Label Presets Reference

When you select a preset, the following labels are automatically applied. You can also override any preset with custom labels using the "Custom Labels Override" field.

1. Sentiment

Perfect for analyzing customer sentiment across reviews, feedback, and social media posts.

• Sentiment Positive
• Sentiment Neutral
• Sentiment Negative

2. Mobile App Reviews

Comprehensive categorization for app store reviews and mobile application feedback.

• UX - User experience issues and feedback
• UI - User interface design feedback
• Feature Request - Requests for new features
• Bug Report - Technical bugs and errors
• Performance - Speed, responsiveness, and optimization issues
• Security - Security concerns and vulnerabilities
• Content - Content quality and relevance
• Pricing - Pricing and subscription feedback
• Support - Customer support experiences
• Onboarding - First-time user experience
• Navigation - App navigation and menu structure
• Search - Search functionality and results
• Notifications - Push notification preferences and issues
• Compatibility - Device and OS compatibility issues
• Other - Miscellaneous feedback

3. Customer Support

Ideal for automating support ticket routing and categorization.

• Question - General inquiries
• Complaint - Customer complaints
• Suggestion - Feature or service suggestions
• Praise - Positive feedback and compliments
• Technical Issue - Technical problems requiring IT support
• Billing - Payment and billing questions
• Refund - Refund requests
• Account - Account management issues
• Product Inquiry - Questions about products or services
• Feature Request - Requests for new features
• Bug Report - Software bugs and errors
• Feedback - General feedback
• Escalation - Issues requiring escalation
• Follow-up - Follow-up on previous interactions
• Other - Miscellaneous support requests

4. E-commerce Reviews

Comprehensive categorization for product reviews and e-commerce feedback.

• Product Quality - Quality assessment of products
• Shipping - Shipping experience and delivery
• Price - Pricing feedback and value perception
• Customer Service - Customer service experience
• Packaging - Product packaging quality
• Delivery Speed - Speed of delivery
• Product Description - Accuracy of product descriptions
• Return Process - Return and refund process experience
• Website Experience - Online shopping experience
• Payment - Payment process and security
• Discount - Discount codes and promotions
• Recommendation - Product recommendations
• Comparison - Comparisons with other products
• Durability - Product longevity and durability
• Other - Miscellaneous review topics

5. Content Moderation

Essential for automated content moderation and safety monitoring.

• Spam - Spam content
• Harassment - Harassing or bullying content
• Hate Speech - Hateful or discriminatory content
• Violence - Violent or threatening content
• Adult Content - Adult or explicit content
• Copyright - Copyright infringement concerns
• Misinformation - False or misleading information
• Off-topic - Content not relevant to the context
• Appropriate - Content that is appropriate
• Inappropriate - Content that is inappropriate
• Needs Review - Content requiring manual review
• Approved - Content that has been approved
• Rejected - Content that has been rejected
• Flagged - Content that has been flagged for review
• Other - Other moderation categories

6. Social Media

Perfect for social media monitoring and engagement analysis.

• Engagement - High engagement content
• Trending - Trending topics and content
• Viral - Viral or highly shared content
• Controversial - Controversial or divisive content
• Educational - Educational or informative content
• Entertainment - Entertainment-focused content
• News - News and current events
• Opinion - Opinion pieces and commentary
• Personal - Personal updates and stories
• Promotional - Promotional or marketing content
• Question - Questions and inquiries
• Answer - Answers and responses
• Complaint - Complaints and negative feedback
• Praise - Positive feedback and compliments
• Other - Other social media content types

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📥 Input Configuration

The Actor expects a JSON input defining the texts to classify and classification settings.

Example Input

{

  "bulkTexts": "I absolutely love this product!\nThe service was terrible.\nIt works as expected.",

  "labelPreset": "sentiment",

  "labels": ""

}

Input Parameters

Parameter	Type	Required	Default	Description
bulkTexts	String	✅ Yes	-	Paste your texts here, one per line. Each line will be classified separately. Supports any number of texts.
labelPreset	String	❌ No	"sentiment"	Select a classification preset. Options: sentiment, mobile_app_reviews, customer_support, ecommerce_reviews, content_moderation, social_media. See the Label Presets section for complete label lists.
labels	String	❌ No	""	Custom Labels Override: Leave empty to use preset labels automatically. Fill this field only if you want to override the preset with your own custom labels (comma-separated). Example: "Technology, Business, Sports, Entertainment, Politics".

How Presets Work

1. If you select a preset (e.g., "Sentiment"): The preset labels are automatically applied, even if the "Custom Labels Override" field appears empty.

2. If you fill "Custom Labels Override": Your custom labels will override the preset. Use comma-separated format: "Label1, Label2, Label3".

3. If no preset is selected and no custom labels: The actor defaults to the "Sentiment" preset.

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📤 Output

The Actor outputs a dataset with classification results for each text. Results are pushed in real-time as they're processed.

Output Fields

Field	Type	Description
index	Integer	Sequential number of the text (1, 2, 3, ...)
text	String	Original text that was classified
classification	String	The assigned classification label (or null if classification failed)
status	String	Classification status: "success" or "error"
error	String	Error message if classification failed (or null if successful)

Example Output

[

  {

    "index": 1,

    "text": "I absolutely love this product!",

    "classification": "Sentiment Positive",

    "status": "success",

    "error": null

  },

  {

    "index": 2,

    "text": "The service was terrible.",

    "classification": "Sentiment Negative",

    "status": "success",

    "error": null

  },

  {

    "index": 3,

    "text": "It works as expected.",

    "classification": "Sentiment Neutral",

    "status": "success",

    "error": null

  }

]

Dataset Views

The output dataset includes three pre-configured views:

• Overview: All classification results with index, text, classification, and status.

• Successful Classifications: Only successfully classified texts with their assigned labels.

• Errors: Failed classifications with error messages for troubleshooting.

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