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Da San Martino G., Gao W., Sebastiani F. Ordinal text quantification. In: SIGIR 2016 - 39th International ACM SIGIR conference on Research and Development in Information Retrieval (Pisa, Italy, 17-21 July 2016). Proceedings, pp. 937 - 940. Raffaele Perego, Fabrizio Sebastiani (eds.). ACM, 2016.
 
 
Abstract
(English)
In recent years there has been a growing interest in text quantification, a supervised learning task where the goal is to accurately estimate, in an unlabelled set of items, the prevalence (or "relative frequency") of each class c in a predefined set C. Text quantification has several applications, and is a dominant concern in fields such as market research, the social sciences, political science, and epidemiology. In this paper we tackle, for the first time, the problem of ordinal text quantification, defined as the task of performing text quantification when a total order is defined on the set of classes; estimating the prevalence of "five stars" reviews in a set of reviews of a given product, and monitoring this prevalence across time, is an example application. We present OQT, a novel tree-based OQ algorithm, and discuss experimental results obtained on a dataset of tweets classified according to sentiment strength.
URL: http://dl.acm.org/citation.cfm?doid=2911451.2914749
DOI: 10.1145/2911451.2914749
Subject Quantification
I.2.6 ARTIFICIAL INTELLIGENCE. Learning


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