MonkeyLearn

MonkeyLearn was an AI-powered text analysis and machine learning platform designed to help businesses turn unstructured text into structured, actionable data. It was particularly useful for analyzing customer feedback, support tickets, emails, chats, surveys, social media posts, reviews, and other text-based information. The platform combined pre-built machine-learning models with tools for creating custom classifiers and extractors, allowing organizations to automate text categorization and analysis without building machine-learning systems from scratch.

MonkeyLearn was founded in 2014 and became known for making text analytics accessible to business teams and developers. Its platform allowed users to create models for sentiment analysis, topic classification, intent detection, keyword extraction, entity extraction, and other natural-language processing tasks. Users could work with their own datasets and create custom categories and tags to organize information according to their business requirements.

A major use of MonkeyLearn was automated text classification. Businesses could train models to identify topics or categories in large collections of text. For example, customer-service teams could automatically categorize support tickets according to issue type, while product teams could analyze customer feedback to identify recurring problems or requests.

Sentiment analysis was another important capability. MonkeyLearn could examine text and determine whether the expressed sentiment was positive, negative, or neutral. This made it useful for monitoring customer satisfaction, analyzing survey responses, evaluating reviews, and understanding reactions to products or services.

The platform also provided text extraction capabilities. Instead of simply assigning a category to a document, users could extract specific information such as keywords, entities, and other relevant data. This helped organizations convert unstructured documents into structured information that could subsequently be analyzed or transferred into business workflows.

MonkeyLearn supported custom machine-learning models as well as pre-built models. This combination allowed organizations to begin with ready-made text-analysis capabilities and create more specialized models when their requirements were more specific. The platform was therefore suitable for both straightforward sentiment analysis and more customized business classification projects.

Data could be imported from common business sources, including CSV files, Excel spreadsheets, and Google Sheets. The platform also connected with external business applications through integrations and automation services. Insight Platforms identifies Google Sheets, CSV, Excel, Zapier, and Google-related workflows among the platform’s supported data and integration options.

MonkeyLearn was particularly notable for its API. Developers could send text to MonkeyLearn’s machine-learning models programmatically and receive classification or extraction results. The API supported classifiers and extractors, making it possible to incorporate text analysis into websites, applications, internal systems, and automated workflows. A Python client was also available for interacting with the API.

The platform could also be used through integrations rather than requiring users to build their own software connections. Zendesk integration, for example, allowed MonkeyLearn to analyze support tickets and apply tags to them. This made it possible to automatically organize customer-service requests and incorporate machine-learning results into existing support workflows.

MonkeyLearn’s visual analytics capabilities helped users explore the results of their text analysis. Classified and extracted information could be organized into dashboards, charts, and reports, giving teams a way to identify patterns across large amounts of textual information.

The platform was useful across customer experience, customer support, marketing, product development, research, and business analytics. Support teams could use it to classify and prioritize tickets. Marketing teams could analyze reviews and social comments. Product teams could identify recurring feature requests and complaints. Researchers could process large collections of survey responses and other textual datasets.

MonkeyLearn was also designed to reduce the amount of manual work required to process text. Instead of employees reading and manually categorizing thousands of comments, machine-learning models could perform repetitive classification and extraction tasks automatically. This made the platform valuable for organizations dealing with large volumes of unstructured information.

However, MonkeyLearn’s standalone product is no longer the current commercial offering. Medallia acquired MonkeyLearn in February 2022, and MonkeyLearn’s text-analysis technology became part of Medallia’s experience-management ecosystem. Current Medallia Text Analytics provides similar capabilities around themes, sentiment, natural-language understanding, customer feedback, and unstructured text analysis.

Because of this transition, older MonkeyLearn listings and pricing information should be treated as historical rather than current standalone pricing. Historical software directories list plans beginning around $299 per feature per month, while other historical information describes API and Studio offerings. These figures should not be interpreted as a current MonkeyLearn subscription price.

For users researching MonkeyLearn as a historical AI text-analysis product, it remains an important example of no-code machine-learning text analytics. For organizations looking for the currently maintained successor technology, Medallia’s Text Analytics platform is the relevant product direction. Medallia currently provides text analytics for themes, sentiment, emerging trends, customer feedback, and other unstructured data, with support for multiple languages and industry-specific models.

Pricing

MonkeyLearn no longer has a current standalone pricing structure because the product was acquired by Medallia and its capabilities were incorporated into Medallia’s broader experience-management platform. Historical pricing information from software directories lists MonkeyLearn at approximately $299 per feature per month for its paid offering, with custom pricing also associated with higher-level requirements.

Pricing Model

Subscription / Contact for pricing

Pricing Note

The historical MonkeyLearn product had subscription-based pricing, but MonkeyLearn is no longer sold as an independent standalone platform. Current Medallia Text Analytics pricing is handled as part of Medallia’s enterprise platform and generally requires contacting Medallia for a quote.

Review

MonkeyLearn was a strong text-analysis platform for organizations that wanted to apply machine learning to large amounts of unstructured text without building everything themselves. Its combination of custom classifiers, extractors, sentiment analysis, topic classification, dashboards, integrations, and API access made it useful for both business users and developers.

One of its biggest strengths was flexibility. Users could start with pre-built models and then create custom models around their own categories and terminology. The API also allowed developers to incorporate MonkeyLearn’s text-analysis capabilities into their own applications and workflows.

Its integrations were another advantage. Connections with tools such as Google Sheets, Excel, Zendesk, and Zapier made it easier to bring machine-learning results into existing business processes.

The main limitation today is availability. MonkeyLearn should not be evaluated as a currently independent SaaS product because the standalone platform has been absorbed into Medallia. Organizations looking for comparable current functionality should instead evaluate Medallia Text Analytics or other active text-analysis platforms.