Text Clustering is MeaningCloud's solution for automatic document clustering, i.e., the task of grouping a set of texts in such a way that texts in the same group (called a cluster) are more similar to each other than to those in other clusters.
The algorithm receives a set of texts and returns the list of detected clusters. Each cluster is assigned a descriptive name, a relevance value (indicating the relative importance of the cluster with respect to all clusters), its size, and the list of elements that are included in the cluster. Each document may be assigned to one or several clusters.
Text clustering may be used for different tasks, such as grouping similar documents (news, tweets, etc.) and the analysis of customer/employee feedback, discovering meaningful implicit subjects across all documents.
The current supported languages are Spanish, English, French, Italian, Portuguese, Catalan, Danish, Swedish, Norwegian, Finnish, Chinese, Russian and Arabic. The API is configured for a general purpose clustering task and includes software developed by the Carrot2 Project.
Click on the version number to see the changelog.