Ensemble method for online sentiment classification using drift detection-based adaptive window method

Textual data streams have been widely applied in real-world applications where online users’ expressed their opinions for online products. Mining this stream of data is a challenging task for researchers as a result of changes in data distribution, a phenomenon widely known as concept drift. Most of...

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Main Authors: Rabiu, Idris, Salim, Naomie, Nasser, Maged, Saeed, Faisal, Alromema, Waseem, Awal, Aisha, Joseph, Elijah, Mishra, Amit
Format: Conference or Workshop Item
Published: 2022
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Online Access:http://eprints.utm.my/id/eprint/99878/
http://dx.doi.org/10.1007/978-3-030-98741-1_11
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Institution: Universiti Teknologi Malaysia
id my.utm.99878
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spelling my.utm.998782023-03-19T11:42:18Z http://eprints.utm.my/id/eprint/99878/ Ensemble method for online sentiment classification using drift detection-based adaptive window method Rabiu, Idris Salim, Naomie Nasser, Maged Saeed, Faisal Alromema, Waseem Awal, Aisha Joseph, Elijah Mishra, Amit QA75 Electronic computers. Computer science Textual data streams have been widely applied in real-world applications where online users’ expressed their opinions for online products. Mining this stream of data is a challenging task for researchers as a result of changes in data distribution, a phenomenon widely known as concept drift. Most of the existing classification methods incorporated drift detection methods that depend on the classification errors. However, these methods are prone to higher false-positive or missed detections rates. Thus, there is a need for more sensitive detection methods that can detect the maximum number of drifts in the data stream to improve classification accuracy. In this paper, we present a drift detection-based adaptive windowing for ensemble classifier, an adaptive unsupervised learning algorithm for sentiment classification, and opinion mining. The proposed algorithm employs four different dissimilarity measures to quantify the magnitude of concept drift in data streams, to improve the classification performance. Series of the experiments were conducted on the real-world datasets and the results demonstrated the efficiency of our proposed model. 2022 Conference or Workshop Item PeerReviewed Rabiu, Idris and Salim, Naomie and Nasser, Maged and Saeed, Faisal and Alromema, Waseem and Awal, Aisha and Joseph, Elijah and Mishra, Amit (2022) Ensemble method for online sentiment classification using drift detection-based adaptive window method. In: 6th International Conference of Reliable Information and Communication Technology (IRICT 2021), 22 - 23 December 2021, Virtual, Online28. http://dx.doi.org/10.1007/978-3-030-98741-1_11
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Rabiu, Idris
Salim, Naomie
Nasser, Maged
Saeed, Faisal
Alromema, Waseem
Awal, Aisha
Joseph, Elijah
Mishra, Amit
Ensemble method for online sentiment classification using drift detection-based adaptive window method
description Textual data streams have been widely applied in real-world applications where online users’ expressed their opinions for online products. Mining this stream of data is a challenging task for researchers as a result of changes in data distribution, a phenomenon widely known as concept drift. Most of the existing classification methods incorporated drift detection methods that depend on the classification errors. However, these methods are prone to higher false-positive or missed detections rates. Thus, there is a need for more sensitive detection methods that can detect the maximum number of drifts in the data stream to improve classification accuracy. In this paper, we present a drift detection-based adaptive windowing for ensemble classifier, an adaptive unsupervised learning algorithm for sentiment classification, and opinion mining. The proposed algorithm employs four different dissimilarity measures to quantify the magnitude of concept drift in data streams, to improve the classification performance. Series of the experiments were conducted on the real-world datasets and the results demonstrated the efficiency of our proposed model.
format Conference or Workshop Item
author Rabiu, Idris
Salim, Naomie
Nasser, Maged
Saeed, Faisal
Alromema, Waseem
Awal, Aisha
Joseph, Elijah
Mishra, Amit
author_facet Rabiu, Idris
Salim, Naomie
Nasser, Maged
Saeed, Faisal
Alromema, Waseem
Awal, Aisha
Joseph, Elijah
Mishra, Amit
author_sort Rabiu, Idris
title Ensemble method for online sentiment classification using drift detection-based adaptive window method
title_short Ensemble method for online sentiment classification using drift detection-based adaptive window method
title_full Ensemble method for online sentiment classification using drift detection-based adaptive window method
title_fullStr Ensemble method for online sentiment classification using drift detection-based adaptive window method
title_full_unstemmed Ensemble method for online sentiment classification using drift detection-based adaptive window method
title_sort ensemble method for online sentiment classification using drift detection-based adaptive window method
publishDate 2022
url http://eprints.utm.my/id/eprint/99878/
http://dx.doi.org/10.1007/978-3-030-98741-1_11
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