Application of the random dichotomous classification method to assessment builder’s brand based on consumer’s reviews

Abstract

The brand influences the success and sustainability of the builder. In this paper, the authors continue to address the problem of managing developer branding based on information on the Internet. The paper proposes a dichotomous classification that allows you to classify developer reviews based on randomly labeled data. he dichotomous classification method allows you to work with small databases, in contrast to methods based on the construction of artificial neural networks, which require large samples of data. The method was implemented in the "Eidos" system. The Eidos system is based on automated system cognitive analysis (ASC-analysis). The decision to change the recall marker was made on the basis of changing the credibility of the model by Van Riesbergen's F-measure. The application of the dichotomous classification method to classify developers' reviews was tested on the example of OJSC "Perm Silicate Panels Plant" (PZSP). The dichotomous classification method allowed us to distinguish 20 negative and 63 positive reviews in the collected sample. The performance and adequacy of the method used is shown. In particular, it is shown that it is possible to divide the collected textual information about the developer into two clusters without prior training. The dichotomous classification method reduces the developer's response time to a reference. This paper proposes an algorithm for reference clustering based on the dichotomous classification method. This algorithm can be used as a basis for software to collect and analyze developer data, as well as to manage developer branding.