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Exploring the Ensemble of Classifiers for Sentimental Analysis: A Systematic Literature Review

Authors:

Wasi Haider Butt,

Muhammad Waseem Anwar,

Muhammad Latif,

Farooque AzamAuthors Info & Claims

ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

Pages 410 - 414

https://doi.org/10.1145/3055635.3056601

Published: 24 February 2017 Publication History

Abstract

Text classification is a well-known machine learning approach to simplify the domain-specific investigation. Therefore, it is commonly utilized in the field of sentimental analysis to achieve the particular business goals. Different ensemble approaches are frequently introduced to unify the desired classifiers for the improvement of sentimental classification. However, to the best of our knowledge, no study is available yet that investigate and summarize the leading ensemble approaches, classifiers, features, tools and datasets altogether in the domain of sentimental analysis. Therefore, in this paper, a Systematic Literature Review (SLR) is performed to identify 31 studies published during 2008--2016. Subsequently, 14 modern ensemble techniques, 26 leading classifiers, 15 benchmark datasets, 19 prominent features and 8 tools are presented in the context of sentimental analysis. This investigation certainly benefits the scholars and industrial experts of the domain while deciding the right choices according to the given requirements.

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Cited By

Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
https://doi.org/10.1017/S0269888923000024
Latif MQamar U(2019)A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental AnalysisIntelligent Computing10.1007/978-3-030-22871-2_39(573-592)Online publication date: 23-Jun-2019
https://doi.org/10.1007/978-3-030-22871-2_39

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ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

February 2017

545 pages

ISBN:9781450348171

DOI:10.1145/3055635

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

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Published: 24 February 2017

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ICMLC 2017

ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing

February 24 - 26, 2017

Singapore, Singapore

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Cited By

Cagnini HDas Dôres SFreitas ABarros R(2023)A survey of evolutionary algorithms for supervised ensemble learningThe Knowledge Engineering Review10.1017/S026988892300002438Online publication date: 1-Mar-2023
https://doi.org/10.1017/S0269888923000024
Latif MQamar U(2019)A Novel Ensemble Approach for Feature Selection to Improve and Simplify the Sentimental AnalysisIntelligent Computing10.1007/978-3-030-22871-2_39(573-592)Online publication date: 23-Jun-2019
https://doi.org/10.1007/978-3-030-22871-2_39

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