MonkeyLearn is a Machine Learning platform on the cloud for Text Analysis. It allows companies and developers to easily get actionable data from raw text, for example, you can detect topic or sentiment expressed in texts like tweets, chats, reviews, articles and more.
- A Graphical User Interface on the web that allows to easily:Create and test customized machine learning algorithms to solve particular problems.Pick already created algorithms for common problems (sentiment analysis, topic detection, etc).Combine customized and pre-created modules to build high level solutions.
- A scalable cloud computing platform where machine learning algorithms can be trained and ran instantly without installing or deploying any software.
- An API and SDKs (Python, Ruby, Node, Java and PHP) that allows to integrate MonkeyLearn cloud computing engine with any software project, using any programming language.
- A documentation and blog that show to the average user how to use MonkeyLearn for their particular problems
One of the most important features about MonkeyLearn is that you can train a Machine Learning algorithm on the fly with your particular data. That means that by training on particular domain data, you may get much better accuracy in tasks like text classification, e.g. topic detection and sentiment analysis.
MonkeyLearn functionalities are organized in two families of modules:
- Classification: modules that take text and return labels or categories organized in a certain hierarchy.
- Extraction: modules that extract particular data within a text, for example: entities, addresses, keywords and more.
Much of the documentation and terminology in our platform will refer to modules as classifiers or extractors.