Master MonkeyLearn with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make MonkeyLearn powerful for ai data workflows.
Pre-trained and custom sentiment analysis models that detect emotional tone, polarity, and intensity in text data across customer feedback channels. The models classify text as positive, negative, or neutral and can be fine-tuned with domain-specific training data to recognize industry jargon and context-dependent expressions that generic models miss.
Analyze customer feedback sentiment, monitor brand perception, or evaluate product reviews for emotional insights
Automatic multi-label categorization of text content into user-defined topics and themes, powered by machine learning classifiers that improve with additional training data. The system supports hierarchical topic taxonomies and can assign multiple relevant topics to a single piece of text, enabling nuanced content organization beyond simple keyword matching.
Organize customer support tickets, categorize survey responses, or segment content for targeted analysis
Named entity recognition engine that identifies and extracts specific data points like company names, product references, monetary values, locations, and custom entities from unstructured text. Extracted entities can be used to build structured databases from free-form customer feedback or to power downstream analytics and reporting workflows.
Extract key information from customer feedback, identify mentioned products, or analyze competitive mentions
Visual interface for creating, training, and deploying custom text analysis models without programming. Users upload labeled examples, define classification categories or extraction rules, and iteratively refine model performance through an interactive accuracy testing workflow that highlights misclassifications and suggests improvements.
Enable business analysts and non-technical team members to build domain-specific text classifiers tailored to their organization's unique terminology and categorization needs
Pre-built connectors and API access for embedding text analysis into existing business workflows across tools like Google Sheets, Zendesk, Freshdesk, Zapier, and custom applications. Automated pipelines can process incoming data in real time or batch mode, applying trained models and routing results to dashboards, databases, or notification systems without manual intervention.
Automatically analyze and tag new support tickets as they arrive in Zendesk, or process survey responses in Google Sheets with sentiment scores appended to each row
Unified text analysis across multiple data sources including customer surveys, social media posts, online reviews, support tickets, chat transcripts, and email correspondence. The platform normalizes text from diverse formats and channels into a consistent analytical framework, enabling cross-channel sentiment comparison and trend detection from a single dashboard.
Compare customer sentiment across social media, email support, and survey channels to identify where experience gaps are most acute and prioritize improvements by channel impact
MonkeyLearn as an independent, standalone text analysis platform is no longer available for new customers. After being acquired by Medallia, the technology was integrated into Medallia's enterprise experience management platform. Existing MonkeyLearn users were transitioned to Medallia's ecosystem. If you are looking for MonkeyLearn's text analysis capabilities today, you would need to explore Medallia's platform offerings, which bundle text analytics with broader customer and employee experience management tools at enterprise-level pricing.
Following the acquisition, MonkeyLearn's standalone API endpoints and direct integrations were gradually sunset as the technology was absorbed into Medallia's platform. Developers who previously used the MonkeyLearn REST API for sentiment analysis or text classification need to migrate to Medallia's API infrastructure. The core NLP capabilities remain available but are now accessed through Medallia's platform APIs and SDKs, which have different authentication, rate limiting, and endpoint structures than the original MonkeyLearn API.
MonkeyLearn's pre-trained models offered competitive accuracy for common tasks like sentiment analysis and topic classification, typically performing well on English-language customer feedback and support data. Custom-trained models could achieve higher accuracy when provided with sufficient domain-specific labeled data, with performance varying depending on the complexity of the classification task and quality of training data. However, for highly specialized or multilingual use cases, dedicated NLP platforms or large language model-based solutions may provide better out-of-the-box performance.
Since MonkeyLearn's standalone offering is no longer available, small businesses seeking similar no-code text analysis capabilities should consider alternatives such as AWS Comprehend for cloud-native NLP, Google Cloud Natural Language API for general text analysis, or specialized tools like Lexalytics, MeaningCloud, or Aylien for text analytics. For teams that prefer a visual, no-code approach similar to MonkeyLearn's original interface, platforms like Levity or Obviously AI offer accessible machine learning model building without coding requirements.
Custom text classification model training, which was one of MonkeyLearn's signature features, is still available within Medallia's platform but is geared toward enterprise deployments. The process involves uploading labeled training data, configuring classification categories, and iterating on model accuracy through Medallia's analytics suite. However, the self-service simplicity that made MonkeyLearn popular with individual users and small teams has been replaced by an enterprise-oriented workflow that typically involves Medallia's professional services team for initial setup and model configuration.
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Tutorial updated March 2026