Multiclass Text Categorization for Automated Survey Coding

Survey coding is the task of assigning a symbolic code from a predefined set of such codes to the answer given in response to an open-ended question in a questionnaire (aka survey). We formulate the problem of automated survey coding as a text categorization problem, i.e. as the problem of learning, by means of supervised machine learning techniques, a model of the association between answers and codes from a training set of pre-coded answers, and applying the resulting model to the classification of new answers. In this paper we experiment with two different learning techniques, one based on na¨ıve Bayesian classification and the other one based on multiclass support vector machines, and test the resulting framework on a corpus of social surveys. The results we have obtained significantly outperform the results achieved by previous automated survey coding approaches. Keywords Open-ended survey coding, multiclass text categorization

• Type: Conference
• Year: 2003
• Where: SAC
• Authors: Daniela Giorgetti, Fabrizio Sebastiani
• Keywords: Applied Computing, Learning Techniques, SAC 2003, Survey Coding, Text Categorization