Text based classification of companies in CrunchBase

• Main Author: Batista, F.
• Publication Date: 2015
• Other Authors: João P. Carvalho
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10071/25098
• topic: Text classification; Fuzzy fingerprints; Text mining; Crunchbase; Document classification

This paper introduces two fuzzy fingerprint based text classification techniques that were successfully applied to automatically label companies from CrunchBase, based purely on their unstructured textual description. This is a real and very challenging problem due to the large set of possible labels (more than 40) and also to the fact that the textual descriptions do not have to abide by any criteria and are, therefore, extremely heterogeneous. Fuzzy fingerprints are a recently introduced technique that can be used for performing fast classification. They perform well in the presence of unbalanced datasets and can cope with a very large number of classes. In the paper, a comparison is performed against some of the best text classification techniques commonly used to address similar problems. When applied to the CrunchBase dataset, the fuzzy fingerprint based approach outperformed the other techniques.