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Automating survey coding by multiclass text categorization techniques (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Giorgetti, Daniela Linguistica, Istituto Di Sebastiani, Fabrizio |
| Abstract | 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). This task is usually carried out in order to group respondents according to a predefined scheme based on their answers. Survey coding has several applications, especially in the social sciences, ranging from the simple classification of respondents to the extraction of statistics on political opinions, health and lifestyle habits, customer satisfaction, brand fidelity, and patient satisfaction. Survey coding is a difficult task, since the code that should be attributed to a respondent based on the answer she has given is a matter of subjective judgment, and thus requires expertise. It is thus unsurprising that this task has traditionally been performed manually, by trained coders. Some attempts have been made at automating this task, most of them based on detecting the similarity between the answer and textual descriptions of the meanings of the candidate codes. We take a radically new stand, and 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 naïve 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. 1 |
| File Format | |
| Volume Number | 54 |
| Journal | Journal of the American Society for Information Science and Technology |
| Language | English |
| Publisher Date | 2003-01-01 |
| Access Restriction | Open |
| Subject Keyword | Survey Coding Multiclass Text Categorization Technique Textual Description Automated Survey Coding Subjective Judgment Candidate Code Brand Fidelity Symbolic Code Trained Coder Several Application Predefined Set Aka Survey Patient Satisfaction Difficult Task Social Science Text Categorization Problem Lifestyle Habit Customer Satisfaction New Stand Pre-coded Answer Political Opinion Open-ended Question Simple Classification Group Respondent New Answer Training Set Multiclass Support Vector Machine Na Ve Bayesian Classification Social Survey Supervised Machine |
| Content Type | Text |
| Resource Type | Article |
