Automatically Generated Classifiers for Opinion Mining with Different Term Weighting Schemes

Shakhnaz Akhmedova, Eugene Semenkin, Roman Sergienko

Abstract

Automatically generated classifiers using different term weighting schemes for Opinion Mining are presented. New collective nature-inspired self-tuning meta-heuristic for solving unconstrained and constrained real- and binary-parameter optimization problems called Co-Operation of Biology Related Algorithms was developed and used for classifiers design. Three Opinion Mining problems from DEFT’07 competition were solved by proposed classifiers. Also different weighting schemes were used for data processing. Obtained results were compared between themselves and with results obtained by methods which were proposed by other researchers. As the result workability and usefulness of designed classifiers were established and best data processing approach for them was found.

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Paper Citation


in Harvard Style

Akhmedova S., Semenkin E. and Sergienko R. (2014). Automatically Generated Classifiers for Opinion Mining with Different Term Weighting Schemes . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014) ISBN 978-989-758-040-6, pages 845-850. DOI: 10.5220/0005148508450850


in Bibtex Style

@conference{asaahmi14,
author={Shakhnaz Akhmedova and Eugene Semenkin and Roman Sergienko},
title={Automatically Generated Classifiers for Opinion Mining with Different Term Weighting Schemes},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014)},
year={2014},
pages={845-850},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005148508450850},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014)
TI - Automatically Generated Classifiers for Opinion Mining with Different Term Weighting Schemes
SN - 978-989-758-040-6
AU - Akhmedova S.
AU - Semenkin E.
AU - Sergienko R.
PY - 2014
SP - 845
EP - 850
DO - 10.5220/0005148508450850