Ensembles of Classifiers for Parallel Categorization of Large Number of Text Documents Expressing Opinions

František Dařena and Jan Zizka ()
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Jan Zizka: Department of Informatics, Faculty of Business and Economics, Mendel Uni- versity in Brno, Zemedelska 1, 613 00 Brno, Czech Republic

No 2016-65, MENDELU Working Papers in Business and Economics from Mendel University in Brno, Faculty of Business and Economics

Abstract: Opinions provided by people that used some services or purchased some goods are a rich source of knowledge. The opinion classification, applying mostly supervised classifiers, is one of the essential tasks. Computer’s technological capabilities are still a major obstacle, especially when processing huge volumes of data. This study proposes and evaluates experimentally a parallelism application to the classification of a very large number of contrary opinions expressed as freely written text reviews. Instead of training a single classifier on the entire data set, an ensemble of classifiers is trained on disjunctive subsets of data and a group decision is used for the classification of unlabelled items. The main assessment criteria are computational efficiency and error rates, combined into a single measure to be able to compare ensembles of different sizes. Support vector machines, artificial neural networks, and deci- sion trees, belonging to frequently used classification methods, were examined. The paper demonstrates the suggested method viability when the number of text reviews leads to com- putational complexity, which is beyond the contemporary common PC’s capabilities. Classification accuracy and the values of other classification performance measures (Precision, Recall, F-measure) did not decrease, which is a positive finding.

Keywords: text documents; natural language; classification; parallel processing; ensembles of classifiers; machine learning (search for similar items in EconPapers)
JEL-codes: C38 C89 (search for similar items in EconPapers)
Pages: 17
Date: 2016-12
New Economics Papers: this item is included in nep-cmp
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