You can easily classify texts by topic, sentiment or intent or extract specific data such as keywords, names, and companies.

MonkeyLearn makes teams more efficient by automating business processes, getting insights and saving hours of manual text data processing.

Text Classification

Text classification models are used to categorize text into organized groups. Text is analyzed by a model and then the appropriate tags are applied based on the content. 

You can classify texts with custom categories or tags for sentiment analysis, topic detection, product classification, aspects detection and much more.

With MonkeyLearn, you can use our library of publicly available classifiers and you can also make your own custom classifier for your own specific use case.

Text Extraction

Extraction models are used to extract data from text, that is, the result you are looking for exists within the text. 

The difference with extraction compared to classification is that in classification the result is an associated tag that is usually not present within the text, and therefore has to be predicted or deduced from the text contents.

MonkeyLearn has different extraction models to extract different types of data: entities, company names, keywords, addresses, emails, etc. You may work with the extraction models publicly available to resolve your particular problem. Or you can create your own custom extractor.